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Osong B, Hasannejadasl H, van der Poel H, Vanneste B, van Roermund J, Aben K, Van Soest J, Van Oort I, Hochstenbach L, Bloemen- van Gurp EJ, Dekker A, Fijten RR. The value of PROMs for predicting erectile dysfunction in prostate cancer patients with Bayesian network. Tech Innov Patient Support Radiat Oncol 2024; 31:100234. [PMID: 39188594 PMCID: PMC11345401 DOI: 10.1016/j.tipsro.2024.100234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/03/2023] [Accepted: 01/04/2024] [Indexed: 08/28/2024] Open
Abstract
Purpose This study aims to develop and externally validate a clinically plausible Bayesian network structure to predict one-year erectile dysfunction in prostate cancer patients by combining expert knowledge with evidence from data using clinical and Patient-reported outcome measures (PROMs) data. In addition, compare and contrast structures that stem from PROM information and routine clinical data. Summary of background For men with localized prostate cancer, choosing the optimal treatment can be challenging since each option comes with different side effects, such as erectile dysfunction, which negatively impacts their quality of life. Our previous findings demonstrate that logistic regression models are able to identify patients at high risk of erectile dysfunction. However, methods such as Bayesian networks may be more successful, as they intricately represent the causal relations between the variables. Patients and methods 946 prostate cancer patients from 65 Dutch hospitals were considered to develop the Bayesian network structure. Continuous variables were discretized before analysis based on expert opinions and literature. Patients with missing information and variables with more than 25% of missing information were excluded. Prostate cancer treating physicians first determined the relationships (arcs) between the available variables. The structures were then modified based on algorithmically derived structures using the hill-climbing algorithm. Structural Performance was evaluated based on the area under the curve (AUC) values and calibration plots on the training and test data. Results BMI and prostate volume via MRI were excluded from this analysis due to their high percentage of missingness (>45 %). The final cohort was reduced to 505 and 216 after excluding 157 and 68 patients with missing information, respectively. The AUC of the PROM structure was better than the clinical structure in both the train and test data. The structure that combined both sources of information had an AUC value of 0.94 (0.92 - 0.96) and 0.84171 (0.77 91) in the train and test data, respectively. Conclusion Bayesian network structures derived from PROM information by complimenting expert knowledge with evidence from the data produce a clinically plausible structure that is more performant than structures from clinical data. Our study supports the growing global recognition of incorporating the patient's perspective in outcomes research for better decision-making and optimal outcomes. However, a structure that combines both sources of information gives a more holistic view of the patient with actionable insights and improved discriminative power.
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Affiliation(s)
- Biche Osong
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Hajar Hasannejadasl
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Henk van der Poel
- Department of Urology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ben Vanneste
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Joep van Roermund
- Department of Urology, Maastricht University Medical Center, the Netherlands
| | - Katja Aben
- Department of Research Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Johan Van Soest
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Inge Van Oort
- Department of Urology, Radboud university Medical Center, Nijmegen, the Netherlands
| | - Laura Hochstenbach
- Department of Urology, Maastricht University Medical Center, the Netherlands
| | - Esther J. Bloemen- van Gurp
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
- Department of Urology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Urology, Maastricht University Medical Center, the Netherlands
- Department of Research Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Rianne R.R. Fijten
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
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Wang JQ, Li Q, He JY, Zhou F, Huang ZH, Wang LB, Zhang Y, Li X. Autophagy in Multiple Sclerosis: Phagocytosis and Autophagy of Oligodendrocyte Precursor Cells. Mol Neurobiol 2024; 61:6920-6933. [PMID: 38363533 DOI: 10.1007/s12035-024-03996-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024]
Abstract
Multiple sclerosis (MS) is a leading cause of chronic neurological dysfunction in young to middle-aged adults, affecting approximately 2.5 million people worldwide. It is characterized by inflammation, multifocal demyelination, axonal loss, and white and gray matter gliosis. Autophagy is a highly conserved protein degradation pathway. Polymorphisms in autophagy-related genes have been implicated in a variety of autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, psoriasis and MS. However, the significance of autophagy in MS remains to be elucidated. This paper aims to explore the potential role of autophagy-related genes in MS diseases by using bioinformatics combined with machine learning methods. Finally, we obtained 9 autophagy genes with the highest correlation with MS, and further changes in these autophagy genes were verified in the experimental autoimmune encephalomyelitis (EAE) model and oligodendrocyte precursor cells (OPCs) engulfed myelin debris (MD). Combined with bioinformatic analysis and experimental data, Becn1 showed obvious expression abnormalities suggesting that this gene has vital functions in autophagy and MD engulfed by OPCs. This work will be of great significance for the further exploration of autophagy-related genes in demyelinating diseases.
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Affiliation(s)
- Jia-Qi Wang
- National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Qiang Li
- College of Urban and Environmental Sciences, Northwest University, Xi'an, China
| | - Jia-Yi He
- National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Fang Zhou
- National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Zi-Hao Huang
- National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Li-Bin Wang
- The Nervous System Disease Diagnosis and Treatment Engineering Technology Research Center of Ningxia, Yinchuan, 750001, China
| | - Yuan Zhang
- National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Xing Li
- National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, China.
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Feng B, Zhang Y, Qiao L, Tang Q, Zhang Z, Zhang S, Qiu J, Zhou X, Huang C, Liang Y. Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment. Front Immunol 2024; 15:1426875. [PMID: 39170615 PMCID: PMC11335526 DOI: 10.3389/fimmu.2024.1426875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 07/03/2024] [Indexed: 08/23/2024] Open
Abstract
Background The main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms. Methods In this study, transcriptomic data from intestinal mucosal biopsies underwent Robust Rank Aggregation (RRA) analysis to identify differential genes. These genes intersected with UC key genes from Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning identified UC signature genes, aiding predictive model development. Validation involved external data for diagnostic, progression, and drug efficacy assessment, along with ELISA testing of clinical serum samples. Results RRA integrative analysis identified 251 up-regulated and 211 down-regulated DEGs intersecting with key UC genes in WGCNA, yielding 212 key DEGs. Subsequently, five UC signature biomarkers were identified by machine learning based on the key DEGs-THY1, SLC6A14, ECSCR, FAP, and GPR109B. A logistic regression model incorporating these five genes was constructed. The AUC values for the model set and internal validation data were 0.995 and 0.959, respectively. Mechanistically, activation of the IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway in UC was indicated by KEGG and GSVA analyses, which were positively correlated with the signature biomarkers. Additionally, the expression of the signature biomarkers was strongly correlated with various UC types and drug efficacy in different datasets. Notably, ECSCR was found to be upregulated in UC serum and exhibited a positive correlation with neutrophil levels in UC patients. Conclusions THY1, SLC6A14, ECSCR, FAP, and GPR109B can serve as potential biomarkers of UC and are closely related to signaling pathways associated with UC progression. The discovery of these markers provides valuable information for understanding the molecular mechanisms of UC.
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Affiliation(s)
- Bin Feng
- Center for Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yanqiu Zhang
- Institute of Clinical Pharmacology, Anhui Medical University, Key Laboratory of Anti-inflammatory and Immune Medicine, Ministry of Education, Anhui Collaborative Innovation Center of Anti-inflammatory and Immune Medicine, Hefei, Anhui, China
| | - Longwei Qiao
- Center for Reproduction and Genetics, School of Gusu, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Nanjing Medical University, Suzhou, Jiangsu, China
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Qingqin Tang
- Center for Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Zheng Zhang
- Center for Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Sheng Zhang
- Center for Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Jun Qiu
- Center for Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xianping Zhou
- Department of Laboratory, Bozhou Hospital Affiliated to Anhui Medical University, Bozhou, Anhui, China
- Department of Laboratory, Anhui Medical University, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Chao Huang
- Center for Reproduction and Genetics, School of Gusu, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Yuting Liang
- Center for Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Lin S, Yan J, Wang W, Luo L. STAT3-Mediated Ferroptosis is Involved in Sepsis-Associated Acute Respiratory Distress Syndrome. Inflammation 2024; 47:1204-1219. [PMID: 38236387 DOI: 10.1007/s10753-024-01970-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/04/2024] [Accepted: 01/06/2024] [Indexed: 01/19/2024]
Abstract
Sepsis-induced acute respiratory distress syndrome (ARDS) poses a grave danger to life, resulting from sepsis-induced multi-organ failure. Although ferroptosis, a form of iron-dependent lipid peroxidative cell death, has been associated with sepsis-induced ARDS, the specific mechanisms are not fully understood. In this study, we utilized WGCNA, PPI, friends analysis, and six machine learning techniques (Lasso, SVM, RFB, XGBoost, AdaBoost, and LightGBM) to pinpoint STAT3 as a potential diagnostic marker. A significant increase in monocyte and neutrophil levels was observed in patients with sepsis-induced ARDS, as revealed by immune infiltration analyses, when compared to controls. Moreover, there was a positive correlation between STAT3 expression and the level of infiltration. Single-cell analysis uncovered a notable disparity in B-cell expression between sepsis and sepsis-induced ARDS. Furthermore, in vitro experiments using LPS-treated human bronchial epithelial cells (BEAS-2B) and THP1 cells demonstrated a significant increase in STAT3 phosphorylation expression. Additionally, the inhibition of STAT3 phosphorylation by Stattic effectively prevented LPS-induced ferroptosis in both BEAS-2B and THP1 cells. This indicates that the activation of STAT3 phosphorylation promotes ferroptosis in human bronchial epithelial cells in response to LPS. In summary, this research has discovered and confirmed STAT3 as a potential biomarker for the diagnosis and treatment of sepsis-induced ARDS.
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Affiliation(s)
- Shanshan Lin
- The Marine Biomedical Research Institute, The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Jiayu Yan
- The Marine Biomedical Research Institute, The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Wenjian Wang
- The Marine Biomedical Research Institute, The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Lianxiang Luo
- The Marine Biomedical Research Institute, The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China.
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Wang TW, Hong JS, Huang JW, Liao CY, Lu CF, Wu YT. Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation. Radiother Oncol 2024; 197:110344. [PMID: 38806113 DOI: 10.1016/j.radonc.2024.110344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jia-Sheng Hong
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taiwan.
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Muggiolu G, Sauvaigo S, Libert S, Millet M, Daguenet E, Bouleftour W, Maillet T, Deutsch E, Magné N. Baseline DSB repair prediction of chronic rare Grade ≥ 3 toxicities induced by radiotherapy using classification algorithms. JOURNAL OF RADIATION RESEARCH 2024; 65:540-548. [PMID: 38899572 PMCID: PMC11262860 DOI: 10.1093/jrr/rrae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/29/2024] [Indexed: 06/21/2024]
Abstract
Small fractions of patients suffer from radiotherapy late severe adverse events (AEs Grade ≥ 3), which are usually irreversible and badly affect their quality of life. A novel functional DNA repair assay characterizing several steps of double-strand break (DSB) repair mechanisms was used. DNA repair activities of peripheral blood mononuclear cells were monitored for 1 week using NEXT-SPOT assay in 177 breast and prostate cancer patients. Only seven patients had Grade ≥ 3 AEs, 6 months after radiotherapy initiation. The machine learning method established the importance of variables among demographic, clinical and DNA repair data. The most relevant ones, all related to DNA repair, were employed to build a predictor. Predictors constructed with random forest and minimum bounding sphere predicted late Grade ≥ 3 AEs with a sensitivity of 100% and specificity of 77.17 and 86.22%, respectively. This multiplex functional approach strongly supports a dominant role for DSB repair in the development of chronic AEs. It also showed that affected patients share specific features related to functional aspects of DSB repair. This strategy may be suitable for routine clinical analysis and paves the way for modelling DSB repair associated with severe AEs induced by radiotherapy.
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Affiliation(s)
- Giovanna Muggiolu
- LXRepair, Research Department, 5 Avenue du Grand Sablon, La Tronche 38700, France
| | - Sylvie Sauvaigo
- LXRepair, Research Department, 5 Avenue du Grand Sablon, La Tronche 38700, France
| | - Sarah Libert
- LXRepair, Research Department, 5 Avenue du Grand Sablon, La Tronche 38700, France
| | - Mathias Millet
- LXRepair, Research Department, 5 Avenue du Grand Sablon, La Tronche 38700, France
| | - Elisabeth Daguenet
- Clinical Research Department, Cancerology and Hematology Institute, CHU de Saint Etienne, 108 Avenue Albert Raimond, 42055 Cedex 02, France
| | - Wafa Bouleftour
- Clinical Research Department, Cancerology and Hematology Institute, CHU de Saint Etienne, 108 Avenue Albert Raimond, 42055 Cedex 02, France
| | - Thierry Maillet
- LXRepair, Research Department, 5 Avenue du Grand Sablon, La Tronche 38700, France
| | - Eric Deutsch
- Gustave Roussy Cancer Campus (GRCC), 114 Rue Edouard Vaillant, 94805 Villejuif, France
| | - Nicolas Magné
- Department of Radiation Oncology, Institut Bergonié, 229 Cr de l'Argonne, 33076 Bordeaux, France
- Cellular and Molecular Radiobiology Laboratory, Lyon-Sud Medical School, Unité Mixte de Recherche CNRS5822/IP2I, University of Lyon, Ouliins, 69600, France
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Zhan X, Li J, Ding Y, Zhou F, Zeng R, Lei L, Zhang Y, Feng A, Qu Y, Yang Z. Pyroptosis-related long-noncoding RNA signature predicting survival and immunotherapy efficacy in patients with lung squamous cell carcinoma. Clin Exp Med 2024; 24:145. [PMID: 38960987 PMCID: PMC11222204 DOI: 10.1007/s10238-024-01409-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 06/19/2024] [Indexed: 07/05/2024]
Abstract
Pyroptosis-related long-noncoding RNAs (PRlncRNAs) play an important role in cancer progression. However, their role in lung squamous cell carcinoma (LUSC) is unclear. A risk model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on RNA sequencing data from The Cancer Genome Atlas database. The LUSC cohort was divided into high- and low-risk groups based on the median risk score. For the prognostic value of the model, the Kaplan-Meier analysis, log-rank test, and Cox regression analysis were performed. A nomogram was constructed to predict the prognosis of patients, using a risk score and clinical parameters such as age, sex, clinical stage, and tumor node metastasis classification (TNM) stage. Afterwards, six common algorithms were employed to assess the invasion of immune cells. The Gene Set Enrichment Analysis (GSEA) was conducted to identify differences between patients at high and low risk. Furthermore, the pRRophetic package was employed to forecast the half-maximal inhibitory doses of prevalent chemotherapeutic drugs, while the tumor immune dysfunction and exclusion score was computed to anticipate the response to immunotherapy. The expression levels of the seven PRlncRNAs were examined in both LUSC and normal lung epithelial cell lines using RT-qPCR. Proliferation, migration, and invasion assays were also carried out to investigate the role of MIR193BHG in LUSC cells. Patients in the low-risk group showed prolonged survival in the total cohort or subgroup analysis. The Cox regression analysis showed that the risk model could act as an independent prognostic factor for patients with LUSC. The results of GSEA analysis revealed that the high-risk group showed enrichment of cytokine pathways, Janus tyrosine kinase/signal transducer and activator of the transcription signalling pathway, and Toll-like receptor pathway. Conversely, the low-risk group showed enrichment of several gene repair pathways. Furthermore, the risk score was positively correlated with immune cell infiltration. Moreover, patients in the high-risk category showed reduced responsiveness to conventional chemotherapeutic medications and immunotherapy. The majority of the long noncoding RNAs in the risk model were confirmed to be overexpressed in LUSC cell lines compared to normal lung epithelial cell lines by in vitro tests. Further studies have shown that downregulating the expression of MIR193BHG may inhibit the growth, movement, and infiltration capabilities of LUSC cells, whereas increasing the expression of MIR193BHG could enhance these malignant tendencies. This study found that PRlncRNAs were linked to the prognosis of LUSC patients. The risk model, evaluated across various clinical parameters and treatment modalities, shows potential as a future reference for clinical applications.
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Affiliation(s)
- Xiang Zhan
- Tumor Research and Therapy Center, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Jixian Li
- Tumor Research and Therapy Center, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Yi Ding
- Tumor Research and Therapy Center, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Fengge Zhou
- Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Renya Zeng
- Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Lingli Lei
- Tumor Research and Therapy Center, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Ying Zhang
- Tumor Research and Therapy Center, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Alei Feng
- Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Yan Qu
- Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
| | - Zhe Yang
- Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
- Tumor Research and Therapy Center, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China.
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Carsuzaa F, Chabrillac E, Marcy PY, Mehanna H, Thariat J. Advances and residual knowledge gaps in the neck management of head and neck squamous cell carcinoma patients with advanced nodal disease undergoing definitive (chemo)radiotherapy for their primary. Strahlenther Onkol 2024; 200:553-567. [PMID: 38600366 DOI: 10.1007/s00066-024-02228-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/03/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE Substantial changes have been made in the neck management of patients with head and neck squamous cell carcinomas (HNSCC) in the past century. These have been fostered by changes in cancer epidemiology and technological progress in imaging, surgery, or radiotherapy, as well as disruptive concepts in oncology. We aimed to review changes in nodal management, with a focus on HNSCC patients with nodal involvement (cN+) undergoing (chemo)radiotherapy. METHODS A narrative review was conducted to review current advances and address knowledge gaps in the multidisciplinary management of the cN+ neck in the context of (chemo)radiotherapy. RESULTS Metastatic neck nodes are associated with poorer prognosis and poorer response to radiotherapy, and have therefore been systematically treated by surgery. Radical neck dissection (ND) has gradually evolved toward more personalized and less morbid approaches, i.e., from functional to selective ND. Omission of ND has been made feasible by use of positron-emission tomography/computed tomography to monitor the radiation response in cN+ patients. Human papillomavirus-driven oropharyngeal cancers and their cystic nodes have shown dramatically better prognosis than tobacco-related cancers, justifying a specific prognostic classification (AJCC) creation. Finally, considering the role of lymph nodes in anti-tumor immunity, de-escalation of ND and prophylactic nodal irradiation in combination are intense areas of investigation. However, the management of bulky cN3 disease remains an issue, as aggressive multidisciplinary strategies or innovative combined treatments have not yet significantly improved their prognosis. CONCLUSION Personalized neck management is an increasingly important aspect of the overall therapeutic strategies in cN+ HNSCC.
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Affiliation(s)
- Florent Carsuzaa
- Department of Oto-Rhino-Laryngology & Head and Neck Surgery, Poitiers University Hospital, Poitiers, France
| | - Emilien Chabrillac
- Department of Surgery, University Cancer Institute of Toulouse-Oncopole, Toulouse, France
| | - Pierre Yves Marcy
- Department of Radiology, Clinique du Cap d'Or, La Seyne-sur-mer, France
| | - Hisham Mehanna
- Institute for Head and Neck Studies and Education (InHANSE), University of Birmingham, Birmingham, UK
| | - Juliette Thariat
- Department of radiotherapy, Centre François Baclesse, Caen, France.
- Laboratoire de physique Corpusculaire, IN2P3/ENSICAEN/CNRS, UMR 6534, Normandie Université, Caen, France.
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Zhu J, Shan Y, Li Y, Wu X, Gao G. Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms. World Neurosurg 2024; 185:e1348-e1360. [PMID: 38519020 DOI: 10.1016/j.wneu.2024.03.085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/24/2024]
Abstract
OBJECTIVE This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI). METHODS A single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complex algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale score on the first postoperative day and Glasgow Outcome Scale-Extended score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, 3 multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients' discharge Glasgow Outcome Scale-Extended score. RESULTS The logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge Glasgow Outcome Scale-Extended score, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance. CONCLUSIONS This study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.
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Affiliation(s)
- Jun Zhu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingchi Shan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yihua Li
- Department of Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Wu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guoyi Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Yue W, Wang J, Lin B, Fu Y. Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning. Aging (Albany NY) 2024; 16:7799-7817. [PMID: 38696317 PMCID: PMC11131976 DOI: 10.18632/aging.205783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 12/06/2023] [Indexed: 05/04/2024]
Abstract
Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.
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Affiliation(s)
- Wei Yue
- Innovation Centre for Information, Binjiang Institute of Zhejiang University, Hangzhou 310053, China
| | - Jing Wang
- Innovation Centre for Information, Binjiang Institute of Zhejiang University, Hangzhou 310053, China
| | - Bo Lin
- Innovation Centre for Information, Binjiang Institute of Zhejiang University, Hangzhou 310053, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Yongping Fu
- Department of Cardiovascular Medicine, Affiliated Hospital of Shaoxing University, Shaoxing 312099, China
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Ren W, Liang B, Sun C, Wu R, Men K, Chen H, Feng X, Hou L, Han F, Yi J, Dai J. A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma. Phys Med 2024; 121:103362. [PMID: 38653120 DOI: 10.1016/j.ejmp.2024.103362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 01/27/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE To establish a deep learning-based model to predict radiotherapy-induced temporal lobe injury (TLI). MATERIALS AND METHODS Spatial features of dose distribution within the temporal lobe were extracted using both the three-dimensional convolution (C3D) network and the dosiomics method. The Minimal Redundancy-Maximal-Relevance (mRMR) method was employed to rank the extracted features and select the most relevant ones. Four machine learning (ML) classifiers, including logistic regression (LR), k-nearest neighbors (kNN), support vector machines (SVM) and random forest (RF), were used to establish prediction models. Nested sampling and hyperparameter tuning methods were applied to train and validate the prediction models. For comparison, a prediction model base on the conventional D0.5cc of the temporal lobe obtained from dose volume (DV) histogram was established. The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to compare the predictive performance of the different models. RESULTS A total of 127 nasopharyngeal carcinoma (NPC) patients were included in the study. In the model based on C3D deep learning features, the highest AUC value of 0.843 was achieved with 5 features. For the dosiomics features model, the highest AUC value of 0.715 was attained with 1 feature. Both of these models demonstrated superior performance compared to the prediction model based on DV parameters, which yielded an AUC of 0.695. CONCLUSION The prediction model utilizing C3D deep learning features outperformed models based on dosiomics features or traditional parameters in predicting the onset of TLI. This approach holds promise for predicting radiation-induced toxicities and guide individualized radiotherapy.
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Affiliation(s)
- Wenting Ren
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bin Liang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Runye Wu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kuo Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Huan Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xin Feng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lu Hou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Fei Han
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Junlin Yi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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12
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Yuan L, An L, Zhu Y, Duan C, Kong W, Jiang P, Yu QQ. Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT. Cancer Manag Res 2024; 16:361-375. [PMID: 38699652 PMCID: PMC11063459 DOI: 10.2147/cmar.s451871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
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Affiliation(s)
- Lili Yuan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Lin An
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Yandong Zhu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Chongling Duan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Weixiang Kong
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Qing-Qing Yu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
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Liao W, Xu Y, Pan M, Chen H. Serum micro-RNAs with mutation-targeted RNA modification: a potent cancer detection tool constructed using an optimized machine learning workflow. Sci Rep 2024; 14:9016. [PMID: 38641707 PMCID: PMC11031599 DOI: 10.1038/s41598-024-59480-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/11/2024] [Indexed: 04/21/2024] Open
Abstract
RNA modifications affect fundamental biological processes and diseases and are a research hotspot. Several micro-RNAs (miRNAs) exhibit genetic variant-targeted RNA modifications that can greatly alter their biofunctions and influence their effect on cancer. Therefore, the potential role of these miRNAs in cancer can be implicated in new prevention and treatment strategies. In this study, we determined whether RMvar-related miRNAs were closely associated with tumorigenesis and identified cancer-specific signatures based on these miRNAs with variants targeting RNA modifications using an optimized machine learning workflow. An effective machine learning workflow, combining least absolute shrinkage and selection operator analyses, recursive feature elimination, and nine types of machine learning algorithms, was used to screen candidate miRNAs from 504 serum RMvar-related miRNAs and construct a diagnostic signature for cancer detection based on 43,047 clinical samples (with an area under the curve value of 0.998, specificity of 93.1%, and sensitivity of 99.3% in the validation cohort). This signature demonstrated a satisfactory diagnostic performance for certain cancers and different conditions, including distinguishing early-stage tumors. Our study revealed the close relationship between RMvar-related miRNAs and tumors and proposed an effective cancer screening tool.
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Affiliation(s)
- Wei Liao
- Department of Hepatobiliary Surgery, The First People's Hospital of Foshan, Foshan, Guangdong Province, China
| | - Yuyan Xu
- General Surgery Center, Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Mingxin Pan
- General Surgery Center, Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
| | - Huanwei Chen
- Department of Hepatobiliary Surgery, The First People's Hospital of Foshan, Foshan, Guangdong Province, China.
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Deng X, Luo Y, Lu M, Lin Y, Ma L. Identification of GMFG as a novel biomarker in IgA nephropathy based on comprehensive bioinformatics analysis. Heliyon 2024; 10:e28997. [PMID: 38601619 PMCID: PMC11004809 DOI: 10.1016/j.heliyon.2024.e28997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 03/22/2024] [Accepted: 03/27/2024] [Indexed: 04/12/2024] Open
Abstract
Background IgA nephropathy (IgAN) stands as the most prevalent form of glomerulonephritis and ranks among the leading causes of end-stage renal disease worldwide. Regrettably, we continue to grapple with the absence of dependable diagnostic markers and specific therapeutic agents for IgAN. Therefore, this study endeavors to explore novel biomarkers and potential therapeutic targets in IgAN, while also considering their relevance in the context of tumors. Methods We gathered IgAN datasets from the Gene Expression Omnibus (GEO) database. Subsequently, leveraging these datasets, we conducted an array of analyses, encompassing differential gene expression, weighted gene co-expression network analysis (WGCNA), machine learning, receiver operator characteristic (ROC) curve analysis, gene expression validation, clinical correlations, and immune infiltration. Finally, we carried out pan-cancer analysis based on hub gene. Results We obtained 1391 differentially expressed genes (DEGs) in GSE93798 and 783 DGEs in GSE14795, respectively. identifying 69 common genes for further investigation. Subsequently, GMFG was identified the hub gene based on machine learning. In the verification set and the training set, the GMFG was higher in the IgAN group than in the healthy group and all of the GMFG area under the curve (AUC) was more 0.8. In addition, GMFG has a close relationship with the prognosis of malignancies and a range of immune cells. Conclusions Our study suggests that GMFG could serve as a promising novel biomarker and potential therapeutic target for both IgAN and certain types of tumors.
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Affiliation(s)
- Xiaoqi Deng
- Department of Nephrology, Zigong Fourth People's Hospital, Zigong, 643000, Sichuan Province, China
| | - Yu Luo
- Chongqing Medical University, Chongqing, 400000, China
| | - Meiqi Lu
- School of Medicine, Xiamen University, Xiamen, 361000, China
| | - Yun Lin
- Department of Nephrology, Zigong Fourth People's Hospital, Zigong, 643000, Sichuan Province, China
| | - Li Ma
- Department of Nephrology, Zigong Fourth People's Hospital, Zigong, 643000, Sichuan Province, China
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Xinyang S, Shuang Z, Tianci S, Xiangyu H, Yangyang W, Mengying D, Jingran Z, Feng Y. A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients. Magn Reson Imaging 2024; 107:15-23. [PMID: 38181835 DOI: 10.1016/j.mri.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 09/07/2023] [Accepted: 12/28/2023] [Indexed: 01/07/2024]
Abstract
OBJECTIVES To develop and evaluate a machine learning radiomics model based on biparametric magnetic resonance imaging MRI (bpMRI) to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients. METHODS We retrospectively analyzed bpMRI scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC). RESULTS The LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822-0.974) and an accuracy of 0.828 (95% CI: 0.713-0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784-0.948) and 0.769 (95% CI: 0.648-0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863-0.949) and 0.928 (95% CI: 0.882-0.974) and accuracies of 0.831 (95% CI: 0.727-0.907) and 0.884 (95% CI: 0.792-0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861-0.966), 0.921 (95% CI: 0.889-0.953), and 0.846 (95% CI: 0.735-0.923) and 0.876 (95% CI: 0.771-0.945), respectively. CONCLUSIONS The XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa.
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Affiliation(s)
- Song Xinyang
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Zhang Shuang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441000, China
| | - Shen Tianci
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Hu Xiangyu
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Wang Yangyang
- Department of Orthopedics, Xiangyang No. 1 People's Hospital, Jinzhou Medical University Union Training Base, Xiangyang 441000, China
| | - Du Mengying
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Zhou Jingran
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
| | - Yang Feng
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
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Jeppe K, Ftouni S, Nijagal B, Grant LK, Lockley SW, Rajaratnam SMW, Phillips AJK, McConville MJ, Tull D, Anderson C. Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach. SCIENCE ADVANCES 2024; 10:eadj6834. [PMID: 38457492 PMCID: PMC11094653 DOI: 10.1126/sciadv.adj6834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 02/02/2024] [Indexed: 03/10/2024]
Abstract
Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.
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Affiliation(s)
- Katherine Jeppe
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia
| | - Suzanne Ftouni
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia
| | - Brunda Nijagal
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, Parkville, Australia
| | - Leilah K. Grant
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Steven W. Lockley
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Shantha M. W. Rajaratnam
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Andrew J. K. Phillips
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Malcolm J. McConville
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, Parkville, Australia
| | - Dedreia Tull
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, Parkville, Australia
| | - Clare Anderson
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Edgbaston, UK
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Ye L, Gu L, Wang Y, Xing H, Li P, Guo X, Wang Y, Ma W. Identification of TMZ resistance-associated histone post-translational modifications in glioblastoma using multi-omics data. CNS Neurosci Ther 2024; 30:e14649. [PMID: 38448295 PMCID: PMC10917648 DOI: 10.1111/cns.14649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUD Glioblastoma multiforme (GBM) is among the most aggressive cancers, with current treatments limited in efficacy. A significant hurdle in the treatment of GBM is the resistance to the chemotherapeutic agent temozolomide (TMZ). The methylation status of the MGMT promoter has been implicated as a critical biomarker of response to TMZ. METHODS To explore the mechanisms underlying resistance, we developed two TMZ-resistant GBM cell lines through a gradual increase in TMZ exposure. Transcriptome sequencing of TMZ-resistant cell lines revealed that alterations in histone post-translational modifications might be instrumental in conferring TMZ resistance. Subsequently, multi-omics analysis suggests a strong association between histone H3 lysine 9 acetylation (H3K9ac) levels and TMZ resistance. RESULTS We observed a significant correlation between the expression of H3K9ac and MGMT, particularly in the unmethylated MGMT promoter samples. More importantly, our findings suggest that H3K9ac may enhance MGMT transcription by facilitating the recruitment of the SP1 transcription factor to the MGMT transcription factor binding site. Additionally, by analyzing single-cell transcriptomics data from matched primary and recurrent GBM tumors treated with TMZ, we modeled the molecular shifts occurring upon tumor recurrence. We also noted a reduction in tumor stem cell characteristics, accompanied by an increase in H3K9ac, SP1, and MGMT levels, underscoring the potential role of H3K9ac in tumor relapse following TMZ therapy. CONCLUSIONS The increase in H3K9ac appears to enhance the recruitment of the transcription factor SP1 to its binding sites within the MGMT locus, consequently upregulating MGMT expression and driving TMZ resistance in GBM.
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Affiliation(s)
- Liguo Ye
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Lingui Gu
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yaning Wang
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hao Xing
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Pengtao Li
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiaopeng Guo
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yu Wang
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wenbin Ma
- Department of Neurosurgery, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Tate T, Patlewicz G, Shah I. A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2024; 29:1-14. [PMID: 38993502 PMCID: PMC11235188 DOI: 10.1016/j.comtox.2024.100301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Animal toxicity testing is time and resource intensive, making it difficult to keep pace with the number of substances requiring assessment. Machine learning (ML) models that use chemical structure information and high-throughput experimental data can be helpful in predicting potential toxicity . However, much of the toxicity data used to train ML models is biased with an unequal balance of positives and negatives primarily since substances selected for in vivo testing are expected to elicit some toxicity effect. To investigate the impact this bias had on predictive performance, various sampling approaches were used to balance in vivo toxicity data as part of a supervised ML workflow to predict hepatotoxicity outcomes from chemical structure and/or targeted transcriptomic data. From the chronic, subchronic, developmental, multigenerational reproductive, and subacute repeat-dose testing toxicity outcomes with a minimum of 50 positive and 50 negative substances, 18 different study-toxicity outcome combinations were evaluated in up to 7 ML models. These included Artificial Neural Networks, Random Forests, Bernouilli Naïve Bayes, Gradient Boosting, and Support Vector classification algorithms which were compared with a local approach, Generalised Read-Across (GenRA), a similarity-weighted k-Nearest Neighbour (k-NN) method. The mean CV F1 performance for unbalanced data across all classifiers and descriptors for chronic liver effects was 0.735 (0.0395 SD). Mean CV F1 performance dropped to 0.639 (0.073 SD) with over-sampling approaches though the poorer performance of KNN approaches in some cases contributed to the observed decrease (mean CV F1 performance excluding KNN was 0.697 (0.072 SD)). With under-sampling approaches, the mean CV F1 was 0.523 (0.083 SD). For developmental liver effects, the mean CV F1 performance was much lower with 0.089 (0.111 SD) for unbalanced approaches and 0.149 (0.084 SD) for under-sampling. Over-sampling approaches led to an increase in mean CV F1 performance (0.234, (0.107 SD)) for developmental liver toxicity. Model performance was found to be dependent on dataset, model type, balancing approach and feature selection. Accordingly tailoring ML workflows for predicting toxicity should consider class imbalance and rely on simpler classifiers first.
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Affiliation(s)
- Tia Tate
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
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Kraus KM, Oreshko M, Schnabel JA, Bernhardt D, Combs SE, Peeken JC. Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation. Lung Cancer 2024; 189:107507. [PMID: 38394745 DOI: 10.1016/j.lungcan.2024.107507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 12/08/2023] [Accepted: 02/14/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction. MATERIALS AND METHODS Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds. RESULTS The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose. CONCLUSIONS Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.
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Affiliation(s)
- Kim Melanie Kraus
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany.
| | - Maksym Oreshko
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Medical Faculty, University Hospital, LMU Munich, 80539 Munich, Germany
| | - Julia Anne Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; School of Computation, Information and Technology, Technical University of Munich, Germany; Institute of Machine Learning in Biomedical Imaging, Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany
| | - Stephanie Elisabeth Combs
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany
| | - Jan Caspar Peeken
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany
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Shu P, Liu N, Luo X, Tang Y, Chen Z, Li D, Miao D, Duan J, Yan O, Sheng L, Ouyang G, Wang S, Jiang D, Deng X, Wang Z, Li Q, Wang X. An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence. Front Oncol 2024; 14:1294440. [PMID: 38406803 PMCID: PMC10889124 DOI: 10.3389/fonc.2024.1294440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/24/2024] [Indexed: 02/27/2024] Open
Abstract
Background This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes. Methods Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted. Results In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group. Conclusions The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.
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Affiliation(s)
- Pei Shu
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Clinical Trial Center, National Medical Products Administration Key Laboratory for Clinical Research and Evaluation of Innovative Drugs, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ning Liu
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xu Luo
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Yuanling Tang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhebin Chen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Dandan Li
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dong Miao
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Jiayu Duan
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ouying Yan
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Leiming Sheng
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ganlu Ouyang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Sen Wang
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Dan Jiang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
- Sichuan University-University of Oxford Huaxi Joint Center for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiangbing Deng
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqiang Wang
- Sichuan University-University of Oxford Huaxi Joint Center for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Qingyun Li
- Genecast Biotechnology Co., Ltd., Xishan District, Wuxi, Jiangsu, China
| | - Xin Wang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Sichuan University-University of Oxford Huaxi Joint Center for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Xu H, Sun D, Zhou D, Sun S. Immune Cell Infiltration Types as Biomarkers for the Recurrence Diagnosis and Prognosis of Bladder Cancer. Cancer Invest 2024; 42:186-198. [PMID: 38390837 DOI: 10.1080/07357907.2024.2308161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 01/17/2024] [Indexed: 02/24/2024]
Abstract
This study aimed to investigate the role of infiltrating immune cell types in diagnosing and predicting bladder cancer recurrence. This study mainly applied some algorithms, including Estimate the Proportion of Immune and Cancer Cells (EPIC), support vector machine-recursive feature elimination (SVM-RFE), random forest out-of-bag (RF-OOB) and least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. We found six immune infiltrating cell types significantly associated with recurrence prognosis and two independent clinical prognostic factors. Infiltrating immune cell types (IICTs) based on the prognostic immune risk score (pIRS) models may provide significant biomarkers for the diagnosis and prognostic prediction of bladder cancer recurrence.
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Affiliation(s)
- Hongwei Xu
- Urology Department, Heilongjiang Provincial Hospital, Harbin City, Heilongjiang Province, China
| | - Dapeng Sun
- Urology Department, Heilongjiang Provincial Hospital, Harbin City, Heilongjiang Province, China
| | - Dahong Zhou
- Urology Department, Heilongjiang Provincial Hospital, Harbin City, Heilongjiang Province, China
| | - Shiheng Sun
- Urology Department, Heilongjiang Provincial Hospital, Harbin City, Heilongjiang Province, China
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22
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Salazar RM, Duryea JD, Leone AO, Nair SS, Mumme RP, De B, Corrigan KL, Rooney MK, Das P, Holliday EB, Court LE, Niedzielski JS. Random Forest Modeling of Acute Toxicity in Anal Cancer: Effects of Peritoneal Cavity Contouring Approaches on Model Performance. Int J Radiat Oncol Biol Phys 2024; 118:554-564. [PMID: 37619789 DOI: 10.1016/j.ijrobp.2023.08.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 08/04/2023] [Accepted: 08/13/2023] [Indexed: 08/26/2023]
Abstract
PURPOSE Our purpose was to analyze the effect on gastrointestinal (GI) toxicity models when their dose-volume metrics predictors are derived from segmentations of the peritoneal cavity after different contouring approaches. METHODS AND MATERIALS A random forest machine learning approach was used to predict acute grade ≥3 GI toxicity from dose-volume metrics and clinicopathologic factors for 246 patients (toxicity incidence = 9.5%) treated with definitive chemoradiation for squamous cell carcinoma of the anus. Three types of random forest models were constructed based on different bowel bag segmentation approaches: (1) physician-delineated after Radiation Therapy Oncology Group (RTOG) guidelines, (2) autosegmented by a deep learning model (nnU-Net) following RTOG guidelines, and (3) autosegmented but spanning the entire bowel space. Each model type was evaluated using repeated cross-validation (100 iterations; 50%/50% training/test split). The performance of the models was assessed using area under the precision-recall curve (AUPRC) and the receiver operating characteristic curve (AUROCC), as well as optimal F1 score. RESULTS When following RTOG guidelines, the models based on the nnU-Net auto segmentations (mean values: AUROCC, 0.71 ± 0.07; AUPRC, 0.42 ± 0.09; F1 score, 0.46 ± 0.08) significantly outperformed (P < .001) those based on the physician-delineated contours (mean values: AUROCC, 0.67 ± 0.07; AUPRC, 0.34 ± 0.08; F1 score, 0.36 ± 0.07). When spanning the entire bowel space, the performance of the autosegmentation models improved considerably (mean values: AUROCC, 0.87 ± 0.05; AUPRC, 0.70 ± 0.09; F1 score, 0.68 ± 0.09). CONCLUSIONS Random forest models were superior at predicting acute grade ≥3 GI toxicity when based on RTOG-defined bowel bag autosegmentations rather than physician-delineated contours. Models based on autosegmentations spanning the entire bowel space show further considerable improvement in model performance. The results of this study should be further validated using an external data set.
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Affiliation(s)
- Ramon M Salazar
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Jack D Duryea
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Alexandra O Leone
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Saurabh S Nair
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Raymond P Mumme
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Brian De
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Kelsey L Corrigan
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Michael K Rooney
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Prajnan Das
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Emma B Holliday
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Joshua S Niedzielski
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas.
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Ciobanu-Caraus O, Aicher A, Kernbach JM, Regli L, Serra C, Staartjes VE. A critical moment in machine learning in medicine: on reproducible and interpretable learning. Acta Neurochir (Wien) 2024; 166:14. [PMID: 38227273 DOI: 10.1007/s00701-024-05892-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024]
Abstract
Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.
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Affiliation(s)
- Olga Ciobanu-Caraus
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Anatol Aicher
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julius M Kernbach
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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24
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Chen B, Lin C, Jin X, Zhang X, Yang K, Wang J, Zhang F, Zhang Y, Ji Y, Meng Z. Construction of a diagnostic model for osteoarthritis based on transcriptomic immune-related genes. Heliyon 2024; 10:e23636. [PMID: 38187306 PMCID: PMC10770511 DOI: 10.1016/j.heliyon.2023.e23636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/08/2023] [Accepted: 12/08/2023] [Indexed: 01/09/2024] Open
Abstract
Background Osteoarthritis (OA) is a leading cause of disability globally, affecting over 500 million individuals worldwide. However, accurate and early diagnosis of OA is challenging to achieve. Immune-related genes play an essential role in OA development. Therefore, the objective of this study was to develop a diagnostic model for OA based on immune-related genes identified in synovial membrane. Methods The gene expression profile of OA were downloaded based on four datasets. The significantly differentially expressed genes (DEGs) between OA and control groups were selected. The differential immune cells were analyzed, followed by immune-related DEGs screening. WGCNA was used to screen module genes and these genes were further selected through optimization algorithm. Then, nomogram model was constructed. Chemical drug small molecule related to OA was predicted. Finally, expression levels of several key genes were validated by qRT-PCR through construction of OA rat models. Results The total 656 DEGs were obtained. Eight immune cells were significantly differential between two groups, and 317 immune-related DEGs were obtained. WGCNA identified three modules. The genes in modules were significantly involved in 15 pathways, involving in 65 genes. Then 12 DEGs were screened as the final optimal combination of DEGs, such as CEBPB, CXCL1, JUND, GABARAPL2 and PDGFC. The Nomogram model was also constructed. Furthermore, the chemical small molecules, such as acetaminophen, aspirin, and caffeine were predicted. The expression levels of CEBPB, CXCL1, GABARAPL2 and PDGFC were validated in OA rat models. Conclusion A diagnostic model based on twelve immune related genes was constructed. These model genes, such as CEBPB, CXCL1, GABARAPL2, and PDGFC, may serve as diagnostic biomarkers and immunotherapeutic targets.
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Affiliation(s)
- Bo Chen
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Chun Lin
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Xing Jin
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Xibin Zhang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Kang Yang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Jianjian Wang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Feng Zhang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Yuxin Zhang
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
- Department of Rehabilitation Medicine, Huangpu Branch, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Yingying Ji
- The affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, 214151, China
| | - Zhaoxiang Meng
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
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Shi W, Li T, Li H, Ren J, Lv M, Wang Q, He Y, Yu Y, Liu L, Jin S, Chen H. Bioinformatics approach to identify the hub gene associated with COVID-19 and idiopathic pulmonary fibrosis. IET Syst Biol 2023; 17:336-351. [PMID: 37814484 PMCID: PMC10725713 DOI: 10.1049/syb2.12080] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 09/26/2023] [Accepted: 09/26/2023] [Indexed: 10/11/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS-CoV-2 infection, deserves attention. As COVID-19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID-19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID-19. A risk prediction model was developed to assess the prognosis of patients infected with SARS-CoV-2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS-CoV-2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID-19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID-19. With the increasing availability of COVID-19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.
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Affiliation(s)
- Wenchao Shi
- Department of RespirationThe Fourth Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Tinghui Li
- Department of RespirationHainan Cancer HospitalHaikouHainanChina
| | - Huiwen Li
- Department of RespirationThe Second Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Juan Ren
- Department of RespirationThe Second Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Meiyu Lv
- Department of RespirationThe Fourth Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Qi Wang
- Department of RespirationThe Second Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Yaowu He
- Department of RespirationThe Second Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Yao Yu
- Department of RespirationThe Second Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Lijie Liu
- Department of RespirationThe Fourth Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Shoude Jin
- Department of RespirationThe Fourth Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
| | - Hong Chen
- Department of RespirationThe Second Affiliated Hospital of Harbin Medical UniversityHarbin Medical UniversityHarbinHeilongjiangChina
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Wang YD, Huang CP, Yang YR, Wu HC, Hsu YJ, Yeh YC, Yeh PC, Wu KC, Kao CH. Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer. Diagnostics (Basel) 2023; 13:3380. [PMID: 37958276 PMCID: PMC10648785 DOI: 10.3390/diagnostics13213380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/26/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients' clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. METHODS A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image's region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier's effectiveness, the ROC curve area and confusion matrix were employed. RESULTS Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. CONCLUSIONS The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS.
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Affiliation(s)
- Yu-De Wang
- Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung 404327, Taiwan;
- Department of Urology, China Medical University Hospital, Taichung 404327, Taiwan; (C.-P.H.); (Y.-R.Y.)
| | - Chi-Ping Huang
- Department of Urology, China Medical University Hospital, Taichung 404327, Taiwan; (C.-P.H.); (Y.-R.Y.)
- School of Medicine, China Medical University, Taichung 406040, Taiwan;
| | - You-Rong Yang
- Department of Urology, China Medical University Hospital, Taichung 404327, Taiwan; (C.-P.H.); (Y.-R.Y.)
| | - Hsi-Chin Wu
- School of Medicine, China Medical University, Taichung 406040, Taiwan;
- Department of Urology, China Medical University Beigang Hospital, Yunlin 651012, Taiwan
| | - Yu-Ju Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
| | - Yi-Chun Yeh
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
| | - Pei-Chun Yeh
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
| | - Kuo-Chen Wu
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106319, Taiwan
| | - Chia-Hung Kao
- Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung 404327, Taiwan;
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan
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Bao J, Yu Y. Identification of a prognostic evaluator from glutamine metabolic heterogeneity studies within and between tissues in hepatocellular carcinoma. Front Pharmacol 2023; 14:1241677. [PMID: 37954858 PMCID: PMC10637396 DOI: 10.3389/fphar.2023.1241677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 09/21/2023] [Indexed: 11/14/2023] Open
Abstract
Background: The liver is the major metabolic organ of the human body, and abnormal metabolism is the main factor influencing hepatocellular carcinoma (HCC). This study was designed to determine the effect of glutamine metabolism on HCC heterogeneity and to develop a prognostic evaluator based on the heterogeneity study of glutamine metabolism within HCC tumors and between tissues. Methods: Single-cell transcriptome data were extracted from the GSE149614 dataset and processed using the Seurat package in R for quality control of these data. HCC subtypes in the Cancer Genome Atlas and the GSE14520 dataset were identified via consensus clustering based on glutamine family amino acid metabolism (GFAAM) process genes. The machine learning algorithms gradient boosting machine, support vector machine, random forest, eXtreme gradient boosting, decision trees, and least absolute shrinkage and selection operator were utilized to develop the prognosis model of differentially expressed genes among the molecular gene subtypes. Results: The samples in the GSE149614 dataset included 10 cell types, and there was no significant difference in the GFAAM pathway. HCC was classified into three molecular subtypes according to GFAAM process genes, showing molecular heterogeneity in prognosis, clinicopathological features, and immune cell infiltration. C1 showed the worst survival rate and the highest immune score and immune cell infiltration. A six-gene model for prognostic and immunotherapy responses was constructed among subtypes, and the calculated high-risk score was significantly correlated with poor prognosis, high immune abundance, and a low response rate of immunotherapy in HCC. Conclusion: Our discovery of GFAAM-associated marker genes may help to further decipher the role in HCC occurrence and progression. In particular, this six-gene prognostic model may serve as a predictor of treatment and prognosis in HCC patients.
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Affiliation(s)
- Jie Bao
- Digestive System Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Yu
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Sancar N, Tabrizi SS. Machine learning approach for the detection of vitamin D level: a comparative study. BMC Med Inform Decis Mak 2023; 23:219. [PMID: 37845674 PMCID: PMC10580577 DOI: 10.1186/s12911-023-02323-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/03/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND After the World Health Organization declared the COVID-19 pandemic, the role of Vitamin D has become even more critical for people worldwide. The most accurate way to define vitamin D level is 25-hydroxy vitamin D(25-OH-D) blood test. However, this blood test is not always feasible. Most data sets used in health science research usually contain highly correlated features, which is referred to as multicollinearity problem. This problem can lead to misleading results and overfitting problems in the ML training process. Therefore, the proposed study aims to determine a clinically acceptable ML model for the detection of the vitamin D status of the North Cyprus adult participants accurately, without the need to determine 25-OH-D level, taking into account the multicollinearity problem. METHOD The study was conducted with 481 observations who applied voluntarily to Internal Medicine Department at NEU Hospital. The classification performance of four conventional supervised ML models, namely, Ordinal logistic regression(OLR), Elastic-net ordinal regression(ENOR), Support Vector Machine(SVM), and Random Forest (RF) was compared. The comparative analysis is performed regarding the model's sensitivity to the participant's metabolic syndrome(MtS)'positive status, hyper-parameter tuning, sensitivities to the size of training data, and the classification performance of the models. RESULTS Due to the presence of multicollinearity, the findings showed that the performance of the SVM(RBF) is obviously negatively affected when the test is examined. Moreover, it can be obviously detected that RF is more robust than other models when the variations in the size of training data are examined. This experiment's result showed that the selected RF and ENOR showed better performances than the other two models when the size of training samples was reduced. Since the multicollinearity is more severe in the small samples, it can be concluded that RF and ENOR are not affected by the presence of the multicollinearity problem. The comparative analysis revealed that the RF classifier performed better and was more robust than the other proposed models in terms of accuracy (0.94), specificity (0.96), sensitivity or recall (0.94), precision (0.95), F1-score (0.95), and Cohen's kappa (0.90). CONCLUSION It is evident that the RF achieved better than the SVM(RBF), ENOR, and OLR. These comparison findings will be applied to develop a Vitamin D level intelligent detection system for being used in routine clinical, biochemical tests, and lifestyle characteristics of individuals to decrease the cost and time of vitamin D level detection.
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Affiliation(s)
- Nuriye Sancar
- Department of Mathematics, Near East University, Nicosia, 99138, Turkey.
| | - Sahar S Tabrizi
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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Starke S, Zwanenburg A, Leger K, Lohaus F, Linge A, Kalinauskaite G, Tinhofer I, Guberina N, Guberina M, Balermpas P, von der Grün J, Ganswindt U, Belka C, Peeken JC, Combs SE, Boeke S, Zips D, Richter C, Troost EGC, Krause M, Baumann M, Löck S. Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients. Cancers (Basel) 2023; 15:4897. [PMID: 37835591 PMCID: PMC10571894 DOI: 10.3390/cancers15194897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22-0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18-0.34 and 0.18-0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002 and p=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment.
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Affiliation(s)
- Sebastian Starke
- Helmholtz-Zentrum Dresden–Rossendorf, Department of Information Services and Computing, 01328 Dresden, Germany
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
| | - Alex Zwanenburg
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
| | - Karoline Leger
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
| | - Fabian Lohaus
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
| | - Annett Linge
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
| | - Goda Kalinauskaite
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Berlin, 10117 Berlin, Germany; (G.K.); (I.T.)
- Department of Radiooncology and Radiotherapy, Charité University Hospital, 10117 Berlin, Germany
| | - Inge Tinhofer
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Berlin, 10117 Berlin, Germany; (G.K.); (I.T.)
- Department of Radiooncology and Radiotherapy, Charité University Hospital, 10117 Berlin, Germany
| | - Nika Guberina
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Essen, 45147 Essen, Germany (M.G.)
- Department of Radiotherapy, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
| | - Maja Guberina
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Essen, 45147 Essen, Germany (M.G.)
- Department of Radiotherapy, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
| | - Panagiotis Balermpas
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Frankfurt, 60596 Frankfurt, Germany; (P.B.); (J.v.d.G.)
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, 60596 Frankfurt, Germany
| | - Jens von der Grün
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Frankfurt, 60596 Frankfurt, Germany; (P.B.); (J.v.d.G.)
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, 60596 Frankfurt, Germany
| | - Ute Ganswindt
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- Clinical Cooperation Group, Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany
- Department of Radiation Oncology, Medical University of Innsbruck, Anichstraße 35, A-6020 Innsbruck, Austria
| | - Claus Belka
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- Clinical Cooperation Group, Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany
| | - Jan C. Peeken
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Technische Universität München, 81675 Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Stephanie E. Combs
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Technische Universität München, 81675 Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Simon Boeke
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Tübingen, 72076 Tübingen, Germany; (S.B.); (D.Z.)
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, 72076 Tübingen, Germany
| | - Daniel Zips
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Tübingen, 72076 Tübingen, Germany; (S.B.); (D.Z.)
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, 72076 Tübingen, Germany
| | - Christian Richter
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, 01328 Dresden, Germany
| | - Esther G. C. Troost
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, 01328 Dresden, Germany
| | - Mechthild Krause
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, 01328 Dresden, Germany
| | - Michael Baumann
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- German Cancer Research Center (DKFZ), Division Radiooncology/Radiobiology, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), Core Center DKFZ, 69120 Heidelberg, Germany
| | - Steffen Löck
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
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Ma B, Guo J, Zhai TT, van der Schaaf A, Steenbakkers RJHM, van Dijk LV, Both S, Langendijk JA, Zhang W, Qiu B, van Ooijen PMA, Sijtsema NM. CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma. Med Phys 2023; 50:6190-6200. [PMID: 37219816 DOI: 10.1002/mp.16465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 04/23/2023] [Accepted: 05/01/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches. PURPOSE To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT). METHODS Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans. RESULTS The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. CONCLUSION MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.
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Affiliation(s)
- Baoqiang Ma
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | - Jiapan Guo
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
- Machine Learning Lab, Data Science Centre in Health (DASH), University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands
| | - Tian-Tian Zhai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Arjen van der Schaaf
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | - Roel J H M Steenbakkers
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | - Lisanne V van Dijk
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Centre, Houston, Texas, USA
| | - Stefan Both
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | - Weichuan Zhang
- Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia
| | - Bingjiang Qiu
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
- Machine Learning Lab, Data Science Centre in Health (DASH), University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
- Machine Learning Lab, Data Science Centre in Health (DASH), University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | - Nanna M Sijtsema
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
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Martins JC, Maier J, Gianoli C, Neppl S, Dedes G, Alhazmi A, Veloza S, Reiner M, Belka C, Kachelrieß M, Parodi K. Towards real-time EPID-based 3D in vivo dosimetry for IMRT with Deep Neural Networks: A feasibility study. Phys Med 2023; 114:103148. [PMID: 37801811 DOI: 10.1016/j.ejmp.2023.103148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/17/2023] [Accepted: 09/22/2023] [Indexed: 10/08/2023] Open
Abstract
We investigate the potential of the Deep Dose Estimate (DDE) neural network to predict 3D dose distributions inside patients with Monte Carlo (MC) accuracy, based on transmitted EPID signals and patient CTs. The network was trained using as input patient CTs and first-order dose approximations (FOD). Accurate dose distributions (ADD) simulated with MC were given as training targets. 83 pelvic CTs were used to simulate ADDs and respective EPID signals for subfields of prostate IMRT plans (gantry at 0∘). FODs were produced as backprojections from the EPID signals. 581 ADD-FOD sets were produced and divided into training and test sets. An additional dataset simulated with gantry at 90∘ (lateral set) was used for evaluating the performance of the DDE at different beam directions. The quality of the FODs and DDE-predicted dose distributions (DDEP) with respect to ADDs, from the test and lateral sets, was evaluated with gamma analysis (3%,2 mm). The passing rates between FODs and ADDs were as low as 46%, while for DDEPs the passing rates were above 97% for the test set. Meaningful improvements were also observed for the lateral set. The high passing rates for DDEPs indicate that the DDE is able to convert FODs into ADDs. Moreover, the trained DDE predicts the dose inside a patient CT within 0.6 s/subfield (GPU), in contrast to 14 h needed for MC (CPU-cluster). 3D in vivo dose distributions due to clinical patient irradiation can be obtained within seconds, with MC-like accuracy, potentially paving the way towards real-time EPID-based in vivo dosimetry.
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Affiliation(s)
- Juliana Cristina Martins
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Joscha Maier
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.
| | - Chiara Gianoli
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Sebastian Neppl
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, Munich, 81377, Germany.
| | - George Dedes
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Abdulaziz Alhazmi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Stella Veloza
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, Munich, 81377, Germany.
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, Munich, 81377, Germany.
| | - Marc Kachelrieß
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany; Heidelberg University, Grabengasse 1, Heidelberg, 69117, Germany.
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
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Ecker S, Kirisits C, Schmid M, Knoth J, Heilemann G, De Leeuw A, Sturdza A, Kirchheiner K, Jensen N, Nout R, Jürgenliemk-Schulz I, Pötter R, Spampinato S, Tanderup K, Eder-Nesvacil N. EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer. Radiother Oncol 2023; 186:109748. [PMID: 37330055 DOI: 10.1016/j.radonc.2023.109748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/19/2023]
Abstract
PURPOSE To develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). METHODS A system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. RESULTS EviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. CONCLUSION A novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology.
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Affiliation(s)
- Stefan Ecker
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.
| | - Christian Kirisits
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Maximilian Schmid
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Johannes Knoth
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Gerd Heilemann
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Astrid De Leeuw
- University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands
| | - Alina Sturdza
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Kathrin Kirchheiner
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Nina Jensen
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
| | - Remi Nout
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Ina Jürgenliemk-Schulz
- University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands
| | - Richard Pötter
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Sofia Spampinato
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
| | - Kari Tanderup
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
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Ruiz-Fresneda MA, Gijón A, Morales-Álvarez P. Bibliometric analysis of the global scientific production on machine learning applied to different cancer types. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:96125-96137. [PMID: 37566331 PMCID: PMC10482761 DOI: 10.1007/s11356-023-28576-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/29/2023] [Indexed: 08/12/2023]
Abstract
Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.
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Affiliation(s)
| | - Alfonso Gijón
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
| | - Pablo Morales-Álvarez
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
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Huang B, Chen Q, Ye Z, Zeng L, Huang C, Xie Y, Zhang R, Shen H. Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning. Int J Mol Sci 2023; 24:13175. [PMID: 37685980 PMCID: PMC10487765 DOI: 10.3390/ijms241713175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/10/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023] Open
Abstract
Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-β signaling pathway. Consensus clustering of The Cancer Genome Atlas (TCGA) BRCA dataset using mCAF single-cell characteristic gene signatures segregated samples into high-fibrotic and low-fibrotic groups. Patients in the high-fibrotic group exhibited a significantly poor prognosis. A weighted gene co-expression network analysis and univariate Cox analysis of bulk RNA-seq data revealed 17 differential genes with prognostic values. The mCAF risk prognosis signature (mRPS) was developed using 10 machine learning algorithms. The clinical outcome predictive accuracy of the mRPS was higher than that of the conventional TNM staging system. mRPS was correlated with the infiltration level of anti-tumor effector immune cells. Based on consensus prognostic genes, BRCA samples were classified into the following two subtypes using six machine learning algorithms (accuracy > 90%): interferon (IFN)-γ-dominant (immune C2) and TGF-β-dominant (immune C6) subtypes. Patients with mRPS downregulation were associated with improved prognosis, suggesting that they can potentially benefit from immunotherapy. Thus, the mRPS model can stably predict BRCA prognosis, reflect the local immune status of the tumor, and aid clinical decisions on tumor immunotherapy.
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Affiliation(s)
- Biaojie Huang
- College of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China;
| | - Qiurui Chen
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China; (Q.C.); (Z.Y.); (L.Z.); (C.H.); (Y.X.)
| | - Zhiyun Ye
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China; (Q.C.); (Z.Y.); (L.Z.); (C.H.); (Y.X.)
| | - Lin Zeng
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China; (Q.C.); (Z.Y.); (L.Z.); (C.H.); (Y.X.)
| | - Cuibing Huang
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China; (Q.C.); (Z.Y.); (L.Z.); (C.H.); (Y.X.)
| | - Yuting Xie
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China; (Q.C.); (Z.Y.); (L.Z.); (C.H.); (Y.X.)
| | - Rongxin Zhang
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China; (Q.C.); (Z.Y.); (L.Z.); (C.H.); (Y.X.)
- Institute of Biopharmaceutical Research, Guangdong Pharmaceutical University, Guangzhou 510006, China
| | - Han Shen
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China; (Q.C.); (Z.Y.); (L.Z.); (C.H.); (Y.X.)
- Institute of Biopharmaceutical Research, Guangdong Pharmaceutical University, Guangzhou 510006, China
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Dong X, Liao P, Liu X, Yang Z, Wang Y, Zhong W, Wang B. Construction and Validation of a Reliable Disulfidptosis-Related LncRNAs Signature of the Subtype, Prognostic, and Immune Landscape in Colon Cancer. Int J Mol Sci 2023; 24:12915. [PMID: 37629096 PMCID: PMC10454603 DOI: 10.3390/ijms241612915] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Disulfidptosis, a novel form of regulated cell death (RCD) associated with metabolism, represents a promising intervention target in cancer therapy. While abnormal lncRNA expression is associated with colon cancer development, the prognostic potential and biological characteristics of disulfidptosis-related lncRNAs (DRLs) remain unclear. Consequently, the research aimed to discover a novel indication of DRLs with significant prognostic implications, and to investigate their possible molecular role in the advancement of colon cancer. Here, we acquired RNA-seq data, pertinent clinical data, and genomic mutations of colon adenocarcinoma (COAD) from the TCGA database, and then DRLs were determined through Pearson correlation analysis. A total of 434 COAD patients were divided in to three subgroups through clustering analysis based on DRLs. By utilizing univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression analysis, we ultimately created a prognostic model consisting of four DRLs (AC007728.3, AP003555.1, ATP2B1.AS1, and NSMCE1.DT), and an external database was used to validate the prognostic features of the risk model. According to the Kaplan-Meier curve analysis, patients in the low-risk group exhibited a considerably superior survival time in comparison to those in the high-risk group. Enrichment analysis revealed a significant association between metabolic processes and the genes that were differentially expressed in the high- and low-risk groups. Additionally, significant differences in the tumor immune microenvironment landscape were observed, specifically pertaining to immune cells, function, and checkpoints. High-risk patients exhibited a low likelihood of immune evasion, as indicated by the Tumor Immune Dysfunction and Exclusion (TIDE) analysis. Patients who exhibit both a high risk and high Tumor Mutational Burden (TMB) experience the least amount of time for survival, whereas those belonging to the low-risk and low-TMB category demonstrate the most favorable prognosis. In addition, the risk groups determined by the 4-DRLs signature displayed distinct drug sensitivities. Finally, we confirmed the levels of expression for four DRLs through rt-qPCR in both tissue samples from colon cancer patients and cell lines. Taken together, the first 4-DRLs-based signature we proposed may serve for a hopeful instrument for forecasting the prognosis, immune landscape, and therapeutic responses in colon cancer patients, thereby facilitating optimal clinical decision-making.
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Affiliation(s)
- Xiaoqian Dong
- School of Medicine, Nankai University, Tianjin 300071, China; (X.D.)
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin 300052, China
| | - Pan Liao
- School of Medicine, Nankai University, Tianjin 300071, China; (X.D.)
| | - Xiaotong Liu
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin 300052, China
| | - Zhenni Yang
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin 300052, China
| | - Yali Wang
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin 300052, China
| | - Weilong Zhong
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin 300052, China
| | - Bangmao Wang
- School of Medicine, Nankai University, Tianjin 300071, China; (X.D.)
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin 300052, China
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Qing X, Jiang J, Yuan C, Xie K, Wang K. Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer. Front Endocrinol (Lausanne) 2023; 14:1222072. [PMID: 37664853 PMCID: PMC10471966 DOI: 10.3389/fendo.2023.1222072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/03/2023] [Indexed: 09/05/2023] Open
Abstract
Background Accumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood. Methods This research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model. Results This study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways. Conclusion Taken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.
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Affiliation(s)
- Xin Qing
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
- West China Hospital, Sichuan University, Chengdu, China
| | - Junyi Jiang
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
| | - Chunlei Yuan
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
| | - Kunke Xie
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
| | - Ke Wang
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
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Al-Hammad WE, Kuroda M, Kamizaki R, Tekiki N, Ishizaka H, Kuroda K, Sugimoto K, Oita M, Tanabe Y, Barham M, Sugianto I, Shimizu Y, Nakamitsu Y, Asaumi J. Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients. Curr Oncol 2023; 30:7412-7424. [PMID: 37623018 PMCID: PMC10453557 DOI: 10.3390/curroncol30080537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/08/2023] [Accepted: 07/30/2023] [Indexed: 08/26/2023] Open
Abstract
Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD ≥ 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH.
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Affiliation(s)
- Wlla E. Al-Hammad
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan; (W.E.A.-H.)
- Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Masahiro Kuroda
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Ryo Kamizaki
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Nouha Tekiki
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan; (W.E.A.-H.)
| | - Hinata Ishizaka
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Kazuhiro Kuroda
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
- Department of Health and Welfare Science, Graduate School of Health and Welfare Science, Okayama Prefectural University, Okayama 719-1197, Japan
| | - Kohei Sugimoto
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Masataka Oita
- Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan
| | - Yoshinori Tanabe
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Majd Barham
- Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University, Nablus 44839, Palestine
| | - Irfan Sugianto
- Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University, Sulawesi 90245, Indonesia
| | - Yudai Shimizu
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan; (W.E.A.-H.)
| | - Yuki Nakamitsu
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Junichi Asaumi
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan; (W.E.A.-H.)
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Mu Y, Chen Y, Meng Y, Chen T, Fan X, Yuan J, Lin J, Pan J, Li G, Feng J, Diao K, Li Y, Yu S, Liu L. Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms. Front Oncol 2023; 13:1160383. [PMID: 37601650 PMCID: PMC10436202 DOI: 10.3389/fonc.2023.1160383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/03/2023] [Indexed: 08/22/2023] Open
Abstract
Background Next-generation sequencing (NGS) panels for mature B-cell neoplasms (MBNs) are widely applied clinically but have yet to be routinely used in a manner that is suitable for subtype differential diagnosis. This study retrospectively investigated newly diagnosed cases of MBNs from our laboratory to investigate mutation landscapes in Chinese patients with MBNs and to combine mutational information and machine learning (ML) into clinical applications for MBNs, especially for subtype classification. Methods Samples from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database were collected for ML model construction and cases from our laboratory were used for ML model validation. Five repeats of 10-fold cross-validation Random Forest algorithm was used for ML model construction. Mutation detection was performed by NGS and tumor cell size was confirmed by cell morphology and/or flow cytometry in our laboratory. Results Totally 849 newly diagnosed MBN cases from our laboratory were retrospectively identified and included in mutational landscape analyses. Patterns of gene mutations in a variety of MBN subtypes were found, important to investigate tumorigenesis in MBNs. A long list of novel mutations was revealed, valuable to both functional studies and clinical applications. By combining gene mutation information revealed by NGS and ML, we established ML models that provide valuable information for MBN subtype classification. In total, 8895 cases of 8 subtypes of MBNs in the COSMIC database were collected and utilized for ML model construction, and the models were validated on the 849 MBN cases from our laboratory. A series of ML models was constructed in this study, and the most efficient model, with an accuracy of 0.87, was based on integration of NGS testing and tumor cell sizes. Conclusions The ML models were of great significance in the differential diagnosis of all cases and different MBN subtypes. Additionally, using NGS results to assist in subtype classification of MBNs by method of ML has positive clinical potential.
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Affiliation(s)
- Yafei Mu
- Department of Hematology, The Third Affiliated Hospital of Sun Yat‐sen University and Sun Yat‐sen Institute of Hematology, Guangzhou, China
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
| | - Yuxin Chen
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, China
- Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou, China
| | - Yuhuan Meng
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
- Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou, China
| | - Tao Chen
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
| | - Xijie Fan
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
| | - Jiecheng Yuan
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
| | - Junwei Lin
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
| | - Jianhua Pan
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, China
- Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou, China
| | - Guibin Li
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
| | - Jinghua Feng
- Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, China
| | - Kaiyuan Diao
- Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, China
| | - Yinghua Li
- Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou, China
| | - Shihui Yu
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
- Guangzhou KingMed Transformative Medicine Institute Co., Ltd., Guangzhou, China
- Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, China
- Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou, China
| | - Lingling Liu
- Department of Hematology, The Third Affiliated Hospital of Sun Yat‐sen University and Sun Yat‐sen Institute of Hematology, Guangzhou, China
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Zhuang X, Zhang G, Bao M, Jiang G, Wang H, Li S, Wang Z, Sun X. Development of a novel immune infiltration-related diagnostic model for Alzheimer's disease using bioinformatic strategies. Front Immunol 2023; 14:1147501. [PMID: 37545529 PMCID: PMC10400274 DOI: 10.3389/fimmu.2023.1147501] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Background The pathogenesis of Alzheimer's disease (AD) is complex and multi-factorial. Increasing evidence has shown the important role of immune infiltration in AD. Thus the current study was designed to identify immune infiltration-related genes and to explore their diagnostic value in AD. Methods The expression data of AD patients were downloaded from the GEO database. The limma R package identified differentially expressed genes (DEGs) between AD and controls. The CIBERSORT algorithm identified differentially infiltrated immune cells (DIICs) between AD and controls. DIIC-correlated DEGs were obtained by Pearson correlation analysis. WGCNA was employed to identify DIIC-related modules. Next, LASSO, RFE, and RF machine learning methods were applied to screen robust DIIC-related gene signatures in AD, followed by the construction and validation of a diagnostic nomogram. Detection of the expression of related genes in the peripheral blood of Alzheimer's disease and healthy volunteers by RT-PCR. In addition, the CTD database predicted chemicals targeting DIIC-related gene signatures in the treatment of AD. Results NK cells, M0 macrophages, activated myeloid dendritic cells, resting mast cells, CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, and M2 macrophages were differentially infiltrated between AD and controls. Pearson analysis identified a total of 277 DIIC-correlated DEGs between AD and controls. Thereafter, 177 DIIC-related genes were further obtained by WGCNA analysis. By LASSO, RFE and RF algorithms, CMTM2, DDIT4, LDHB, NDUFA1, NDUFB2, NDUFS5, RPL17, RPL21, RPL26 and NDUFAF2 were identified as robust gene signature in AD. The results of RT-PCR detection of peripheral blood samples from Alzheimer's disease and healthy volunteers showed that the expression trend of ten genes screened was consistent with the detection results; among them, the expression levels of CMTM2, DDIT4, LDHB, NDUFS5, and RPL21 are significantly different among groups. Thus, a diagnostic nomogram based on a DIIC-related signature was constructed and validated. Moreover, candidate chemicals targeting those biomarkers in the treatment of AD, such as 4-hydroxy-2-nonenal, rosiglitazone, and resveratrol, were identified in the CTD database. Conclusion For the first time, we identified 10 immune infiltration-related biomarkers in AD, which may be helpful for the diagnosis of AD and provide guidance in the treatment of AD.
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Affiliation(s)
- Xianbo Zhuang
- Department of Neurology, Liaocheng People’s Hospital and Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, China
| | - Guifeng Zhang
- Department of Neurology, Liaocheng People’s Hospital and Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, China
| | - Mengxin Bao
- Department of Neurology, Liaocheng People’s Hospital and Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, China
| | - Guisheng Jiang
- Department of Neurology, Liaocheng People’s Hospital and Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, China
| | - Huiting Wang
- Department of Neurology, Liaocheng People’s Hospital and Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, China
| | - Shanshan Li
- Clinical Laboratory, Liaocheng Veterans Hospital, Liaocheng, China
| | - Zheng Wang
- Department of Neurosurgery, Liaocheng Traditional Chinese Medicine Hospital, Liaocheng, China
| | - Xiujuan Sun
- Department of Neurology, Liaocheng People’s Hospital and Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, China
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Zauner R, Wimmer M, Atzmueller S, Proell J, Niklas N, Ablinger M, Reisenberger M, Lettner T, Illmer J, Dorfer S, Koller U, Guttmann-Gruber C, Hofbauer JP, Bauer JW, Wally V. Biomarker Discovery in Rare Malignancies: Development of a miRNA Signature for RDEB-cSCC. Cancers (Basel) 2023; 15:3286. [PMID: 37444397 DOI: 10.3390/cancers15133286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/05/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Machine learning has been proven to be a powerful tool in the identification of diagnostic tumor biomarkers but is often impeded in rare cancers due to small patient numbers. In patients suffering from recessive dystrophic epidermolysis bullosa (RDEB), early-in-life development of particularly aggressive cutaneous squamous-cell carcinomas (cSCCs) represents a major threat and timely detection is crucial to facilitate prompt tumor excision. As miRNAs have been shown to hold great potential as liquid biopsy markers, we characterized miRNA signatures derived from cultured primary cells specific for the potential detection of tumors in RDEB patients. To address the limitation in RDEB-sample accessibility, we analyzed the similarity of RDEB miRNA profiles with other tumor entities derived from the Cancer Genome Atlas (TCGA) repository. Due to the similarity in miRNA expression with RDEB-SCC, we used HN-SCC data to train a tumor prediction model. Three models with varying complexity using 33, 10 and 3 miRNAs were derived from the elastic net logistic regression model. The predictive performance of all three models was determined on an independent HN-SCC test dataset (AUC-ROC: 100%, 83% and 96%), as well as on cell-based RDEB miRNA-Seq data (AUC-ROC: 100%, 100% and 91%). In addition, the ability of the models to predict tumor samples based on RDEB exosomes (AUC-ROC: 100%, 93% and 100%) demonstrated the potential feasibility in a clinical setting. Our results support the feasibility of this approach to identify a diagnostic miRNA signature, by exploiting publicly available data and will lay the base for an improvement of early RDEB-SCC detection.
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Affiliation(s)
- Roland Zauner
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Monika Wimmer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Sabine Atzmueller
- Center for Medical Research, Medical Faculty, Johannes-Kepler-University, 4020 Linz, Austria
| | - Johannes Proell
- Center for Medical Research, Medical Faculty, Johannes-Kepler-University, 4020 Linz, Austria
| | - Norbert Niklas
- Red Cross Transfusion Service of Upper Austria, 4020 Linz, Austria
| | - Michael Ablinger
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Manuela Reisenberger
- Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Thomas Lettner
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Julia Illmer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Sonja Dorfer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Ulrich Koller
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Christina Guttmann-Gruber
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Josefina Piñón Hofbauer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Johann W Bauer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
- Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Verena Wally
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology & Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
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Mottin L, Goldman JP, Jäggli C, Achermann R, Gobeill J, Knafou J, Ehrsam J, Wicky A, Gérard CL, Schwenk T, Charrier M, Tsantoulis P, Lovis C, Leichtle A, Kiessling MK, Michielin O, Pradervand S, Foufi V, Ruch P. Multilingual RECIST classification of radiology reports using supervised learning. Front Digit Health 2023; 5:1195017. [PMID: 37388252 PMCID: PMC10303934 DOI: 10.3389/fdgth.2023.1195017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/05/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives The objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages. Methods In our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation. Results The best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks. Conclusions These results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers.
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Affiliation(s)
- Luc Mottin
- HES-SO\HEG Genève, Information Sciences, Geneva, Switzerland
- SIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Jean-Philippe Goldman
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
| | - Christoph Jäggli
- Inselspital – Bern University Hospital and University of Bern, Bern, Switzerland
| | - Rita Achermann
- Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Julien Gobeill
- HES-SO\HEG Genève, Information Sciences, Geneva, Switzerland
- SIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Julien Knafou
- HES-SO\HEG Genève, Information Sciences, Geneva, Switzerland
- SIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Julien Ehrsam
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Alexandre Wicky
- Precision Oncology Center, Oncology Department, Centre Hospitalier Universitaire Vaudois – CHUV, Lausanne, Switzerland
| | - Camille L. Gérard
- Precision Oncology Center, Oncology Department, Centre Hospitalier Universitaire Vaudois – CHUV, Lausanne, Switzerland
| | - Tanja Schwenk
- Department of Oncology, Kantonsspital Aarau, Aarau, Switzerland
| | - Mélinda Charrier
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
| | - Petros Tsantoulis
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Christian Lovis
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Alexander Leichtle
- Inselspital – Bern University Hospital and University of Bern, Bern, Switzerland
| | - Michael K. Kiessling
- Department of Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | - Olivier Michielin
- Precision Oncology Center, Oncology Department, Centre Hospitalier Universitaire Vaudois – CHUV, Lausanne, Switzerland
| | - Sylvain Pradervand
- Precision Oncology Center, Oncology Department, Centre Hospitalier Universitaire Vaudois – CHUV, Lausanne, Switzerland
| | - Vasiliki Foufi
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
| | - Patrick Ruch
- HES-SO\HEG Genève, Information Sciences, Geneva, Switzerland
- SIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva, Switzerland
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Xue J, Zhao H, Fu Y, Liu X, Wu X. Integrated analysis of multiple transcriptomic data identifies ST8SIA6‑AS1 and LINC01093 as potential biomarkers in HBV‑associated liver cancer. Oncol Lett 2023; 25:185. [PMID: 37065781 PMCID: PMC10091192 DOI: 10.3892/ol.2023.13771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/02/2022] [Indexed: 04/18/2023] Open
Abstract
The mechanisms of long-non-coding RNAs (lncRNAs) in hepatitis B virus (HBV) infection-associated liver cancer remain largely unclear. Therefore, the aim of the present study was to investigate the regulatory mechanisms of lncRNAs in this disease. HBV-liver cancer related transcriptome expression profile data (GSE121248 and GSE55092) from the Gene Expression Omnibus database and survival prognosis information from The Cancer Genome Atlas (TCGA) database were obtained for analysis. The limma package was used to identify the overlapped differentially expressed RNAs (DERs), including DElncRNAs and DEmRNAs, in the GSE121248 and GSE55092 datasets. The screened optimized lncRNA signatures were used to develop a nomogram model based on the GSE121248 dataset, which was validated using the GSE55092 and TCGA datasets. A competitive endogenous RNA (ceRNA) network was constructed based on the screened prognosis-associated lncRNA signatures from TCGA dataset. In addition, the levels of specific lncRNAs were evaluated in HBV-infected human liver cancer tissues and cells, and Cell Counting Kit-8, ELISA and Transwell assays were performed to evaluate the effects of the lncRNAs in HBV-expressing liver cancer cells. A total of 535 overlapped DERs, including 30 DElncRNAs and 505 DEmRNAs, were identified in the GSE121248 and GSE55092 datasets. An optimized DElncRNA signature comprising 10 lncRNAs was used to establish a nomogram. ST8SIA6-AS1 and LINC01093 were identified as lncRNAs associated with HBV-liver cancer prognosis in TCGA dataset, and were applied to construct a ceRNA network. Reverse transcription-quantitative PCR analysis showed that ST8SIA6-AS1 was upregulated and LINC01093 was downregulated in HBV-infected human liver cancer tissues and HBV-expressing liver cancer cells compared with non-HBV-infected controls. ST8SIA6-AS1 knockdown and LINC01093 overexpression independently reduced the number of copies of HBV DNA, the levels of hepatitis B surface antigen and hepatitis B e antigen, as well as cell proliferation, migration and invasion. In summary, the present study identified ST8SIA6-AS1 and LINC01093 as two potential biomarkers that may be effective therapeutic targets for HBV-associated liver cancer.
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Affiliation(s)
- Jianhua Xue
- Department of Infectious Diseases, Hospital for Infectious Diseases of Pudong District, Shanghai 201318, P.R. China
| | - Hui Zhao
- Department of Infectious Diseases, Hospital for Infectious Diseases of Pudong District, Shanghai 201318, P.R. China
| | - Yifei Fu
- Department of Infectious Diseases, Hospital for Infectious Diseases of Pudong District, Shanghai 201318, P.R. China
| | - Xu Liu
- Department of Infectious Diseases, Hospital for Infectious Diseases of Pudong District, Shanghai 201318, P.R. China
| | - Xiangxiang Wu
- Department of Traditional Chinese Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, P.R. China
- Correspondence to: Dr Xiangxiang Wu, Department of Traditional Chinese Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou, Shanghai 200437, P.R. China, E-mail:
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Jin Z, Liu YH. Metabolic-related gene signatures for survival prediction and immune cell subtypes associated with prognosis in intrahepatic cholangiocarcinoma. Cancer Genet 2023; 274-275:84-93. [PMID: 37099969 DOI: 10.1016/j.cancergen.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/26/2023] [Accepted: 04/11/2023] [Indexed: 04/28/2023]
Abstract
OBJECTIVES Our study aimed to reveal the metabolic-related gene signatures for survival prediction and immune cell subtypes associated with IHCC prognosis. METHODS Differentially expressed metabolic genes were identified between survival group and dead group which were divided according to survival at discharge. Recursive feature elimination (RFE) and randomForest (RF) algorithms were applied to optimize the combination of feature metabolic genes, which were used to generate SVM classifier. Performance of SVM classifier was evaluated by receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was conducted to uncover the activated pathways in high risk group, and differences in immune cell distributions were revealed. RESULTS There were 143 differentially expressed metabolic gens. RFE and RF identified 21 overlapping differentially expressed metabolic genes, and the constructed SVM classifier had excellent accuracy in training and validation dataset. RS survival prediction model was consisted of 10 metabolic genes. RS model had reliable predictive capability in the training and validation dataset. GSEA revealed 15 significant KEGG pathways that were relatively activated in the high risk group. High risk group had obviously lower counts of B cell naive and T cell CD4+ memory resting, while higher counts of B cell plasma and macrophage M2. CONCLUSION Prognostic prediction model of 10 metabolic genes could accurately predict the prognosis of IHCC patients.
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Affiliation(s)
- Zhe Jin
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, Jilin 130021, China
| | - Ya-Hui Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, Jilin 130021, China.
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Xie H, Wang J, Chen Y, Tu Y, Chen Y, Zhao Y, Zhou P, Wang S, Bai Z, Tang Q. Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning. J Contemp Brachytherapy 2023; 15:134-140. [PMID: 37215613 PMCID: PMC10196730 DOI: 10.5114/jcb.2023.126514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/27/2023] [Indexed: 05/24/2023] Open
Abstract
Purpose The purpose of this study was to investigate the precision of deep learning (DL)-based auto-reconstruction in localizing interstitial needles in post-operative cervical cancer brachytherapy (BT) using three-dimensional (3D) computed tomography (CT) images. Material and methods A convolutional neural network (CNN) was developed and presented for automatic reconstruction of interstitial needles. Data of 70 post-operative cervical cancer patients who received CT-based BT were used to train and test this DL model. All patients were treated with three metallic needles. Dice similarity coefficient (DSC), 95% Hausdorff distance (95% HD), and Jaccard coefficient (JC) were applied to evaluate the geometric accuracy of auto-reconstruction for each needle. Dose-volume indexes (DVI) between manual and automatic methods were used to analyze the dosimetric difference. Correlation between geometric metrics and dosimetric difference was evaluated using Spearman correlation analysis. Results The mean DSC values of DL-based model were 0.88, 0.89, and 0.90 for three metallic needles. Wilcoxon signed-rank test indicated no significant dosimetric differences in all BT planning structures between manual and automatic reconstruction methods (p > 0.05). Spearman correlation analysis demonstrated weak link between geometric metrics and dosimetry differences. Conclusions DL-based reconstruction method can be used to precisely localize the interstitial needles in 3D-CT images. The proposed automatic approach could improve the consistency of treatment planning for post-operative cervical cancer brachytherapy.
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Affiliation(s)
- Hongling Xie
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jiahao Wang
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuanyuan Chen
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yeqiang Tu
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yukai Chen
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yadong Zhao
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Pengfei Zhou
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shichun Wang
- Hangzhou Ruicare MedTech Co., Ltd., Hangzhou, Zhejiang, China
| | - Zhixin Bai
- Hangzhou Ruicare MedTech Co., Ltd., Hangzhou, Zhejiang, China
| | - Qiu Tang
- Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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Kraus KM, Oreshko M, Bernhardt D, Combs SE, Peeken JC. Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition. Front Oncol 2023; 13:1124592. [PMID: 37007119 PMCID: PMC10050584 DOI: 10.3389/fonc.2023.1124592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/01/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionPneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.MethodsWe investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.ResultsResults were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.ConclusionOur results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.
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Affiliation(s)
- Kim Melanie Kraus
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
- *Correspondence: Kim Melanie Kraus,
| | - Maksym Oreshko
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Medical Faculty, University hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
| | - Stephanie Elisabeth Combs
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
| | - Jan Caspar Peeken
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
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Wei Q, Chen Z, Tang Y, Chen W, Zhong L, Mao L, Hu S, Wu Y, Deng K, Yang W, Liu X. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study. Eur Radiol 2023; 33:1906-1917. [PMID: 36355199 DOI: 10.1007/s00330-022-09204-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/20/2022] [Accepted: 09/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVES The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers' discriminative performance for pCR prediction. METHODS This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. RESULTS Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models. CONCLUSIONS The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients. KEY POINTS • A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. • The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. • The RF classifier performed best in the current study.
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Affiliation(s)
- Qiurong Wei
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Zeli Chen
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Yehuan Tang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Weicui Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Liming Zhong
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Liting Mao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Shaowei Hu
- Department of Pathology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kan Deng
- Clinical Science, Philips Healthcare, Guangzhou, China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
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Sun Y, Liu B, Xiao B, Jiang X, Xiang JJ, Xie J, Hu XM. Metabolism-related lncRNAs signature to predict the prognosis of colon adenocarcinoma. Cancer Med 2023; 12:5994-6008. [PMID: 36366731 PMCID: PMC10028123 DOI: 10.1002/cam4.5412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 10/08/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Cell metabolism and long noncoding RNA (lncRNA) played crucial roles in cancer development. However, their association in colon adenocarcinoma (COAD) remains unclear. METHODS The COAD gene expression data and corresponding clinical data were retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of metabolic genes and lncRNA were identified by comparing tumor and normal colon tissues. Pearson correlation analysis was performed to identify metabolism-associated lncRNA. COAD patients were divided into training cohort and validation cohort by randomization. Then, a univariate Cox regression analysis was introduced to evaluate the correlations between metabolism-related lncRNAs and overall survival (OS) of the patients in the training cohort. The least absolute shrinkage and selection operator (LASSO) method was introduced to determine and establish a prognostic prediction model. Subsequently, survival analysis, receiver operating characteristic (ROC) curve analysis, and Cox regression analysis were generated to estimate the prognostic role of the LncRNA risk score in training, validation, and entire cohorts. RESULTS We identified 152 differentially expressed metabolism-associated lncRNAs (MRLncRNAs). A prognostic prediction model involving four metabolism-related lncRNAs were established using LASSO. In each cohort, COAD patients in the high-risk group had worse OS compared to those in the low-risk group. The ROC analyses demonstrated that the lncRNA signature performed well in predicting OS. Uni- and multivariate analysis indicated that the lncRNA signature as an independent prognostic factor. Furthermore, a correlation analysis demonstrated that LINC01138 was the most closely lncRNA related to metabolic genes. In vitro assays demonstrated that LINC01138 affects tumor progression in COAD. CONCLUSIONS In summary, we established a metabolism-associated lncRNAs model to predict the prognosis in COAD patients.
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Affiliation(s)
- Yimin Sun
- Surgery Department of Gastrointestinal, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, People's Republic of China
| | - Bingyan Liu
- Surgery Department of Gastrointestinal, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, People's Republic of China
| | - BaoLai Xiao
- Surgery Department of Gastrointestinal, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, People's Republic of China
| | - XueFeng Jiang
- Surgery Department of Gastrointestinal, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, People's Republic of China
| | - Jin-Jian Xiang
- Surgery Department of Gastrointestinal, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, People's Republic of China
| | - Jianping Xie
- Surgery Department of Gastrointestinal, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, People's Republic of China
| | - Xiao-Miao Hu
- Surgery Department of Gastrointestinal, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, People's Republic of China
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Li F, Zhang Y, Peng Z, Wang Y, Zeng Z, Tang Z. Diagnostic, clustering, and immune cell infiltration analysis of m6A regulators in patients with sepsis. Sci Rep 2023; 13:2532. [PMID: 36781867 PMCID: PMC9925440 DOI: 10.1038/s41598-022-27039-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 12/23/2022] [Indexed: 02/15/2023] Open
Abstract
RNA N6-methladenosine (m6A) regulators are required for a variety of biological processes, including immune responses, and increasing evidence indicates that their dysregulation is closely associated with many diseases. However, the potential roles of m6A regulators in sepsis remain unknown. We comprehensively analyzed the transcriptional variations in and interactions of 26 m6A regulators in sepsis based on the Gene Expression Omnibus (GEO) database. A random forest (RF) model and nomogram were established to predict the occurrence and risk of sepsis in patients. Then, two different m6A subtypes were defined by consensus clustering analysis, and we explored the correlation between the subtypes and immune cells. We found that 17 of the 26 m6A regulators were significantly differentially expressed between patients with and without sepsis, and strong correlations among these 17 m6A regulators were revealed. Compared with the support vector machine (SVM) model, the RF model had better predictive ability, and therefore was used to construct a reliable nomogram containing 10 candidate m6A regulators to predict the risk of sepsis in patients. In addition, a consensus clustering algorithm was used to identify two different subtypes of m6A, which helped us distinguish different levels of immune cell infiltration and inflammation in patients with sepsis. Comprehensive analysis of m6A regulators in sepsis revealed their potential roles in sepsis occurrence, immune cell infiltration and inflammation in patients with sepsis. This study may contribute to the development of follow-up treatment strategies for sepsis.
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Affiliation(s)
- Fenghui Li
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Yuan Zhang
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Zhiyun Peng
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Yingjing Wang
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Zhaoshang Zeng
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Zhongxiang Tang
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China.
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Kotevski DP, Smee RI, Vajdic CM, Field M. Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models. Head Neck 2023; 45:365-379. [PMID: 36369773 PMCID: PMC10100433 DOI: 10.1002/hed.27241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 09/21/2022] [Accepted: 11/02/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Knowledge of the prognostic factors and performance of machine learning predictive models for 2-year cancer-specific survival (CSS) is limited in the head and neck cancer (HNC) population. METHODS Data from our facilities' oncology information system (OIS) collected for routine practice (OIS dataset, n = 430 patients) and research purposes (research dataset, n = 529 patients) were extracted on adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. RESULTS Machine learning demonstrated excellent performance (area under the curve, AUC) in the whole cohort (AUC = 0.97, research dataset), larynx cohort (AUC = 0.98, both datasets), and oropharynx cohort (AUC = 0.99, both datasets). Tumor site and T classification were identified as predictors of 2-year CSS in both datasets. Hypothyroidism and fitness for operation were further identified in the research dataset. CONCLUSIONS Datasets extracted from an OIS for routine clinical practice and research purposes demonstrated high utility for informing 2-year head and neck CSS.
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Affiliation(s)
- Damian P Kotevski
- Department of Radiation Oncology, Prince of Wales Hospital and Community Health Services, Sydney, New South Wales, Australia.,Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Robert I Smee
- Department of Radiation Oncology, Prince of Wales Hospital and Community Health Services, Sydney, New South Wales, Australia.,Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.,Department of Radiation Oncology, Tamworth Base Hospital, Tamworth, New South Wales, Australia
| | - Claire M Vajdic
- Centre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.,Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia
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Chowdhury S, Chen Y, Wen A, Ma X, Dai Q, Yu Y, Fu S, Jiang X, Zong N. Predicting Physiological Response in Heart Failure Management: A Graph Representation Learning Approach using Electronic Health Records. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.27.23285129. [PMID: 36747787 PMCID: PMC9901060 DOI: 10.1101/2023.01.27.23285129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Heart failure management is challenging due to the complex and heterogenous nature of its pathophysiology which makes the conventional treatments based on the "one size fits all" ideology not suitable. Coupling the longitudinal medical data with novel deep learning and network-based analytics will enable identifying the distinct patient phenotypic characteristics to help individualize the treatment regimen through the accurate prediction of the physiological response. In this study, we develop a graph representation learning framework that integrates the heterogeneous clinical events in the electronic health records (EHR) as graph format data, in which the patient-specific patterns and features are naturally infused for personalized predictions of lab test response. The framework includes a novel Graph Transformer Network that is equipped with a self-attention mechanism to model the underlying spatial interdependencies among the clinical events characterizing the cardiac physiological interactions in the heart failure treatment and a graph neural network (GNN) layer to incorporate the explicit temporality of each clinical event, that would help summarize the therapeutic effects induced on the physiological variables, and subsequently on the patient's health status as the heart failure condition progresses over time. We introduce a global attention mask that is computed based on event co-occurrences and is aggregated across all patient records to enhance the guidance of neighbor selection in graph representation learning. We test the feasibility of our model through detailed quantitative and qualitative evaluations on observational EHR data.
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Affiliation(s)
- Shaika Chowdhury
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Yongbin Chen
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Xiao Ma
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Qiying Dai
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Yue Yu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA
| | - Nansu Zong
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
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