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Alghafees M, Seyam RM, Al-Hussain T, Amin TM, Altaweel W, Sabbah BN, Sabbah AN, Almesned R, Alessa L. Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients. Urol Ann 2024; 16:94-97. [PMID: 38415235 PMCID: PMC10896329 DOI: 10.4103/ua.ua_32_23] [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: 03/07/2023] [Revised: 08/24/2023] [Accepted: 10/13/2023] [Indexed: 02/29/2024] Open
Abstract
Objectives Gastrointestinal stromal tumors (GISTs) can occur synchronously with other neoplasms, including the genitourinary (GU) system. Machine learning (ML) may be a valuable tool in predicting synchronous GU tumors in GIST patients, and thus improving prognosis. This study aims to evaluate the use of ML algorithms to predict synchronous GU tumors among GIST patients in a specialist research center in Saudi Arabia. Materials and Methods We analyzed data from all patients with histopathologically confirmed GIST at our facility from 2003 to 2020. Patient files were reviewed for the presence of renal cell carcinoma, adrenal tumors, or other GU cancers. Three supervised ML algorithms were used: logistic regression, XGBoost Regressor, and random forests (RFs). A set of variables, including independent attributes, was entered into the models. Results A total of 170 patients were included in the study, with 58.8% (n = 100) being male. The median age was 57 (range: 9-91) years. The majority of GISTs were gastric (60%, n = 102) with a spindle cell histology. The most common stage at diagnosis was T2 (27.6%, n = 47) and N0 (20%, n = 34). Six patients (3.5%) had synchronous GU tumors. The RF model achieved the highest accuracy with 97.1%. Conclusion Our study suggests that the RF model is an effective tool for predicting synchronous GU tumors in GIST patients. Larger multicenter studies, utilizing more powerful algorithms such as deep learning and other artificial intelligence subsets, are necessary to further refine and improve these predictions.
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Affiliation(s)
- Mohammad Alghafees
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Raouf M Seyam
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Turki Al-Hussain
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Tarek Mahmoud Amin
- Department of Surgical Oncology, Oncology Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Waleed Altaweel
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | | | | | - Razan Almesned
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Laila Alessa
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
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Hua Y, Cai D, Shirley CA, Mo S, Chen R, Gao F, Chen F. A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles. Sci Rep 2023; 13:19429. [PMID: 37940688 PMCID: PMC10632395 DOI: 10.1038/s41598-023-45410-x] [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: 02/16/2023] [Accepted: 10/19/2023] [Indexed: 11/10/2023] Open
Abstract
Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years1. The development of a molecular predictor of early recurrence would allow for the identification of ovarian cancer (OC) patients with high risk of relapse. The Ovarian Cancer Disease Free Survival Predictor (ODFSP), a predictive model constructed from a special set of 1580 OC tumors in which gene expression was assessed using both microarray and sequencing platforms, was created by our team. To construct gene expression barcodes that were resistant to biases caused by disparate profiling platforms and batch effects, we employed a meta-analysis methodology that was based on the binary gene pair technique. We demonstrate that ODFSP is a reliable single-sample predictor of early recurrence (1 year or less) using the largest pool of OC transcriptome data sets available to date. The ODFSP model showed significantly high prognostic value for binary recurrence prediction unaffected by clinicopathologic factors, with a meta-estimate of the area under the receiver operating curve of 0.64 (P = 4.6E-05) and a D-index (robust hazard ratio) of 1.67 (P = 9.2E-06), respectively. GO analysis of ODFSP's 2040 gene pairs (collapsed to 886 distinct genes) revealed the involvement in small molecular catabolic process, sulfur compound metabolic process, organic acid catabolic process, sulfur compound biosynthetic process, glycosaminoglycan metabolic process and aminometabolic process. Kyoto encyclopedia of genes and genomes pathway analysis of ODFSP's signature genes identified prominent pathways that included cAMP signaling pathway and FoxO signaling pathway. By identifying individuals who might benefit from a more aggressive treatment plan or enrolment in a clinical trial but who will not benefit from standard surgery or chemotherapy, ODFSP could help with treatment decisions.
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Affiliation(s)
- Yanjiao Hua
- The Reproductive Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Du Cai
- Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, China
- Guangdong Institute of Gastroenterology, Guangzhou, 510655, Guangdong Province, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong Province, China
| | - Cole Andrea Shirley
- Sun Yat-Sen University, Guangzhou, 510080, Guangdong Province, People's Republic of China
| | - Sien Mo
- The Reproductive Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Ruyun Chen
- Sun Yat-Sen University, Guangzhou, 510080, Guangdong Province, People's Republic of China
| | - Feng Gao
- Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, China
- Guangdong Institute of Gastroenterology, Guangzhou, 510655, Guangdong Province, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong Province, China
| | - Fangying Chen
- Department of Obstetrics and Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong Province, People's Republic of China.
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Huang B, Ding F, Li Y. A practical recurrence risk model based on Lasso-Cox regression for gastric cancer. J Cancer Res Clin Oncol 2023; 149:15845-15854. [PMID: 37672074 DOI: 10.1007/s00432-023-05346-1] [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: 08/03/2023] [Accepted: 08/24/2023] [Indexed: 09/07/2023]
Abstract
INTRODUCTION Gastric cancer remains huge cancer threat worldwide. Detecting the recurrence of gastric cancer after treatment is especially important in improving the prognosis of patients. We aim to fit different risk models with different clinical variables for patients with gastric cancer, which further provides applicable guidance to clinical doctors for their patients. METHODS We collected the primary data from the medical record system in Lanzhou University Second Hospital and further cleaned the primary data via assessing data integrity artificially; meanwhile, detailed conclusion criteria and exclusion criteria were made. We used R software (version 4.1.3) and SPSS 25.0 to analyze data and build models, in which SPSS was used to analyze the correlation and difference of different items in the training set and testing set, and different R packages were used to run LASSO regression, Cox regression and nomogram for variable selection, model construction and model validation. RESULT A total of 649 patients were included in our data analysis and model building. In LASSO regression selection, seven variables, pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, intraoperative blood loss (IBL), the level of AFP and CA199, showed their correlation to the dependent variable. The multivariable Cox regression model fitted using these seven variables showed medium prediction ability, with an AUC of 0.840 in the training set and 0.756 in the testing set. CONCLUSIONS Pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, IBL, the level of AFP and CA199 are significant in identifying recurrence risk for gastric cancer patients after radical gastrectomy.
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Affiliation(s)
- Binjie Huang
- Department of General Surgery, Second Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory of the Digestive System Tumors of Gansu Province, Lanzhou, China
- Lanzhou University, Lanzhou, China
| | - Feifei Ding
- Department of General Surgery, Second Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory of the Digestive System Tumors of Gansu Province, Lanzhou, China
- Lanzhou University, Lanzhou, China
| | - Yumin Li
- Department of General Surgery, Second Hospital of Lanzhou University, Lanzhou, China.
- Key Laboratory of the Digestive System Tumors of Gansu Province, Lanzhou, China.
- Lanzhou University, Lanzhou, China.
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Hayashi K, Ono Y, Takamatsu M, Oba A, Ito H, Sato T, Inoue Y, Saiura A, Takahashi Y. Prediction of Recurrence Pattern of Pancreatic Cancer Post-Pancreatic Surgery Using Histology-Based Supervised Machine Learning Algorithms: A Single-Center Retrospective Study. Ann Surg Oncol 2022; 29:10.1245/s10434-022-11471-x. [PMID: 35230581 DOI: 10.1245/s10434-022-11471-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 02/03/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND Patients with pancreatic cancer (PC) have poor prognosis and a high incidence of recurrence. Since further treatment is applicable for specific recurrent events, it is important to predict recurrence patterns after surgery. This study aimed to identify and predict early and late recurrence patterns of PC using a histology-based machine learning model. PATIENTS AND METHODS Patients who underwent upfront curative surgery for PC between 2001 and 2014 were included. The timing of recurrence and prognosis of each first recurrence site were examined. A histology-based supervised machine learning method, which combined convolutional neural networks and random forest, was used to predict the recurrence and respective sites of metastasis. Accuracy was evaluated using area under the receiver operating characteristic curve (AUC). RESULTS In total, 524 patients were included. Recurrence in the liver accounted for 47.8% of all recurrence events in the first year after surgery. Meanwhile, recurrence in the lung occurred later and could become apparent more than 5 years post-surgery, with indications for further surgery. In terms of substantial distant organ metastases, liver and lung metastases were identified as representative early and late recurrence events. The predictive AUCs of the machine learning model for training and test data were 1.000 and 0.861, respectively, and for predicting nonrecurrence were 1.000 for both. CONCLUSIONS We identified the liver and lung as early and late recurrence sites, which could be distinguished with high probability using a machine learning model. Prediction of recurrence sites using this model may be useful for further treatment of patients with PC.
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Affiliation(s)
- Koki Hayashi
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
| | - Yoshihiro Ono
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
| | - Manabu Takamatsu
- Division of Pathology, Department of Pathology, Cancer Institute, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan.
| | - Atsushi Oba
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
| | - Hiromichi Ito
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
| | - Takafumi Sato
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
| | - Yosuke Inoue
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
| | - Akio Saiura
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan
- Department of Hepatobiliary-Pancreatic Surgery, Juntendo University Hospital, Bunkyo-ku, Tokyo, Japan
| | - Yu Takahashi
- Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan.
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Takamatsu M, Yamamoto N, Kawachi H, Nakano K, Saito S, Fukunaga Y, Takeuchi K. Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence. Sci Rep 2022; 12:2963. [PMID: 35194184 PMCID: PMC8863850 DOI: 10.1038/s41598-022-07038-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/08/2022] [Indexed: 12/13/2022] Open
Abstract
Risk evaluation of lymph node metastasis (LNM) for endoscopically resected submucosal invasive (T1) colorectal cancers (CRC) is critical for determining therapeutic strategies, but interobserver variability for histologic evaluation remains a major problem. To address this issue, we developed a machine-learning model for predicting LNM of T1 CRC without histologic assessment. A total of 783 consecutive T1 CRC cases were randomly split into 548 training and 235 validation cases. First, we trained convolutional neural networks (CNN) to extract cancer tile images from whole-slide images, then re-labeled these cancer tiles with LNM status for re-training. Statistical parameters of the tile images based on the probability of primary endpoints were assembled to predict LNM in cases with a random forest algorithm, and defined its predictive value as random forest score. We evaluated the performance of case-based prediction models for both training and validation datasets with area under the receiver operating characteristic curves (AUC). The accuracy for classifying cancer tiles was 0.980. Among cancer tiles, the accuracy for classifying tiles that were LNM-positive or LNM-negative was 0.740. The AUCs of the prediction models in the training and validation sets were 0.971 and 0.760, respectively. CNN judged the LNM probability by considering histologic tumor grade.
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Affiliation(s)
- Manabu Takamatsu
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan. .,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
| | - Noriko Yamamoto
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hiroshi Kawachi
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kaoru Nakano
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Shoichi Saito
- Department of Endoscopy, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yosuke Fukunaga
- Department of Colorectal Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kengo Takeuchi
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.,Pathology Project for Molecular Targets, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
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Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling. Cancers (Basel) 2021; 13:cancers13174468. [PMID: 34503278 PMCID: PMC8430997 DOI: 10.3390/cancers13174468] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/30/2021] [Accepted: 09/02/2021] [Indexed: 12/26/2022] Open
Abstract
Simple Summary It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options. Abstract Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.
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Emanetci E, Çakır T. Network-Based Analysis of Cognitive Impairment and Memory Deficits from Transcriptome Data. J Mol Neurosci 2021; 71:2415-2428. [PMID: 33713319 DOI: 10.1007/s12031-021-01807-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/01/2021] [Indexed: 12/12/2022]
Abstract
Aging is an inevitable process that negatively affects all living organisms and their vital functions. The brain is one of the most important organs in living beings and is primarily impacted by aging. The molecular mechanisms of learning, memory and cognition are altered over time, and the impairment in these mechanisms can lead to neurodegenerative diseases. Transcriptomics can be used to study these impairments to acquire more detailed information on the affected molecular mechanisms. Here we analyzed learning- and memory-related transcriptome data by mapping it on the organism-specific protein-protein interactome network. Subnetwork discovery algorithms were applied to discover highly dysregulated subnetworks, which were complemented with co-expression-based interactions. The functional analysis shows that the identified subnetworks are enriched with genes having roles in synaptic plasticity, gliogenesis, neurogenesis and cognition, which are reported to be related to memory and learning. With a detailed analysis, we show that the results from different subnetwork discovery algorithms or from different transcriptomic datasets can be successfully reconciled, leading to a memory-learning network that sheds light on the molecular mechanisms behind aging and memory-related impairments.
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Affiliation(s)
- Elif Emanetci
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey.
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