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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024; 57:719-751. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [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] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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Chen R, Liu Y, Xie J. Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism. Heliyon 2024; 10:e37100. [PMID: 39286147 PMCID: PMC11402732 DOI: 10.1016/j.heliyon.2024.e37100] [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: 05/08/2024] [Revised: 08/04/2024] [Accepted: 08/27/2024] [Indexed: 09/19/2024] Open
Abstract
Objective This study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model. Methods From The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model. Results Multivariable cox analysis (HR = 1.488, 95 % CI 1.012-2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008-2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P < 0.001). The high PS group exhibited higher TMB values (P < 0.001). Conclusions The predictive model constructed based on pathomics features can forecast the mRNAsi and survival risk of LUAD. This model holds promise to aid clinical practitioners in identifying high-risk patients and devising more optimized treatment plans for patients by jointly employing therapeutic strategies targeting cancer stem cells (CSCs).
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Affiliation(s)
- Rui Chen
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Donghu District, Nanchang, Jiangxi, 330006, China
| | - Yuzhen Liu
- Department of Oncology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Junping Xie
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Donghu District, Nanchang, Jiangxi, 330006, China
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Yang X, Li X, Xu H, Du S, Wang C, He H. Predicting CTLA4 expression and prognosis in clear cell renal cell carcinoma using a pathomics signature of histopathological images and machine learning. Heliyon 2024; 10:e34877. [PMID: 39145002 PMCID: PMC11320204 DOI: 10.1016/j.heliyon.2024.e34877] [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: 08/08/2023] [Revised: 07/15/2024] [Accepted: 07/17/2024] [Indexed: 08/16/2024] Open
Abstract
Background CTLA4, an immune checkpoint, plays an important role in tumor immunotherapy. The purpose of this study was to develop a pathomics signature to evaluate CTLA4 expression and predict clinical outcomes in clear cell renal cell carcinoma (ccRCC) patients. Methods A total of 354 patients from the TCGA-KIRC dataset were enrolled in this study. The patients were stratified into two groups based on the level of CTLA4 expression, and overall survival rates were analyzed between groups. Pathological features were identified using machine learning algorithms, and a gradient boosting machine (GBM) was employed to construct the pathomics signatures for predicting prognosis and CTLA4 expression. The predictive performance of the model was subsequently assessed. Enrichment analysis was performed on diferentially expressed genes related to the pathomics score (PS). Additionally, correlations between PS and TMB, as well as immune infiltration profiles associated with different PS values, were explored. In vitro experiments, CTLA4 knockdown was performed to investigate its impact on cell proliferation, migration, invasion, TGF-β signaling pathway, and macrophage polarization. Results High expression of CTLA4 was associated with an unfavorable prognosis in ccRCC patients. The pathomics signature displayed good performance in the validation set (AUC = 0.737; P < 0.001 in the log-rank test). The PS was positively correlated with CTLA4 expression. We next explored the underlying mechanism and found the associations between the pathomics signature and TGF-β signaling pathways, TMB, and Tregs. Further in vitro experiments demonstrated that CTLA4 knockdown inhibited cell proliferation, migration, invasion, TGF-β expression, and macrophage M2 polarization. Conclusion High expression of CTLA4 was found to correlate with poor prognosis in ccRCC patients. The pathomics signature established by our group using machine learning effectively predicted both patient prognosis and CTLA4 expression levels in ccRCC cases.
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Affiliation(s)
- Xiaoqun Yang
- Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xiangyun Li
- Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Haimin Xu
- Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Silin Du
- University Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Chaofu Wang
- Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hongchao He
- Department of Urology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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Wu Z, Yang Y, Chen M, Zha Y. Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images. Sci Rep 2024; 14:15065. [PMID: 38956384 PMCID: PMC11220146 DOI: 10.1038/s41598-024-66105-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: 04/18/2024] [Accepted: 06/27/2024] [Indexed: 07/04/2024] Open
Abstract
This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.
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Affiliation(s)
- Zijun Wu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China
| | - Yuan Yang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China
| | - Maojuan Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China.
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Bai C, Sun Y, Zhang X, Zuo Z. Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature. Heliyon 2024; 10:e33107. [PMID: 39022022 PMCID: PMC11253280 DOI: 10.1016/j.heliyon.2024.e33107] [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: 01/21/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 07/20/2024] Open
Abstract
Objective This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD). Methods A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival. Results High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022). Conclusion Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.
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Affiliation(s)
- Cuiqing Bai
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yan Sun
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiuqin Zhang
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhitong Zuo
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
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Yan Z, Li X, Li Z, Liu S, Chang H. Prognostic significance of TNFRSF4 expression and development of a pathomics model to predict expression in hepatocellular carcinoma. Heliyon 2024; 10:e31882. [PMID: 38841483 PMCID: PMC11152671 DOI: 10.1016/j.heliyon.2024.e31882] [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: 09/11/2023] [Revised: 05/16/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024] Open
Abstract
Background TNFRSF4 plays a significant role in cancer progression, especially in hepatocellular carcinoma (HCC). This study aims to investigate the prognostic value of TNFRSF4 expression in patients with HCC and to develop a predictive pathomics model for its expression. Methods A cohort of patients with HCC retrieved from the TCGA database was analyzed using RNA-seq analysis to determine TNFRSF4 expression and its impact on overall survival (OS). Additionally, hematoxylin-eosin staining analysis was performed to construct a pathomics model for predicting TNFRSF4 expression. Then, pathway enrichment analysis was conducted, immune checkpoint markers were investigated, and immune cell infiltration was examined to explore the underlying biological mechanism of the pathomics score. Results TNFRSF4 expression was significantly higher in tumor tissues than in normal tissues. TNFRSF4 expression also exhibited significant correlations with various clinical variables, including pathologic stage III/IV and R1/R2/RX residual tumor. Furthermore, elevated TNFRSF4 expression was associated with unfavorable OS. Interestingly, in the subgroup analysis, elevated TNFRSF4 expression was identified as a significant risk factor for OS in male patients. The newly developed pathomics model successfully predicted TNFRSF4 expression with good performance and revealed a significant association between high pathomics scores and worse OS. In male patients, high pathomics scores were also associated with a higher risk of mortality. Moreover, pathomics scores were also involved in specific hallmarks, immune-related characteristics, and apoptosis-related genes in HCC, such as epithelial-mesenchymal transition, Tregs, and BAX expression. Conclusions Our findings suggest that TNFRSF4 expression and the newly devised pathomics scores hold potential as prognostic markers for OS in patients with HCC. Additionally, gender influenced the association between these markers and patient outcomes.
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Affiliation(s)
- Zhaoyong Yan
- Department of Interventional Radiology, Shaanxi Provincial People's Hospital, Xi'an, 710068, China
| | - Xiang Li
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, 430000, China
| | - Zeyu Li
- Department of General Surgery, Shaanxi Provincial People's Hospital, Xi'an, 710068, China
| | - Sinan Liu
- Department of SICU, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Hulin Chang
- Department of Hepatobiliary Surgery, Shaanxi Provincial People's Hospital, Xi'an, 710068, China
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Mastronikolis NS, Delides A, Kyrodimos E, Piperigkou Z, Spyropoulou D, Giotakis E, Tsiambas E, Karamanos NK. Insights into metastatic roadmap of head and neck cancer squamous cell carcinoma based on clinical, histopathological and molecular profiles. Mol Biol Rep 2024; 51:597. [PMID: 38683372 PMCID: PMC11058607 DOI: 10.1007/s11033-024-09476-8] [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: 12/21/2023] [Accepted: 03/25/2024] [Indexed: 05/01/2024]
Abstract
The incidence of head and neck cancer (HNC), constituting approximately one in ten cancer cases worldwide, affects approximately 644,000 individuals annually. Managing this complex disease involves various treatment modalities such as systemic therapy, radiation, and surgery, particularly for patients with locally advanced disease. HNC treatment necessitates a multidisciplinary approach due to alterations in patients' genomes affecting their functionality. Predominantly, squamous cell carcinomas (SCCs), the majority of HNCs, arise from the upper aerodigestive tract epithelium. The epidemiology, staging, diagnosis, and management techniques of head and neck squamous cell carcinoma (HNSCC), encompassing clinical, image-based, histopathological and molecular profiling, have been extensively reviewed. Lymph node metastasis (LNM) is a well-known predictive factor for HNSCC that initiates metastasis and significantly impacts HNSCC prognosis. Distant metastasis (DM) in HNSCC has been correlated to aberrant expression of cancer cell-derived cytokines and growth factors triggering abnormal activation of several signaling pathways that boost cancer cell aggressiveness. Recent advances in genetic profiling, understanding tumor microenvironment, oligometastatic disease, and immunotherapy have revolutionized treatment strategies and disease control. Future research may leverage genomics and proteomics to identify biomarkers aiding individualized HNSCC treatment. Understanding the molecular basis, genetic landscape, atypical signaling pathways, and tumor microenvironment have enhanced the comprehension of HNSCC molecular etiology. This critical review sheds light on regional and distant metastases in HNSCC, presenting major clinical and laboratory features, predictive biomarkers, and available therapeutic approaches.
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Affiliation(s)
- Nicholas S Mastronikolis
- Department of Otorhinolaryngology - Head and Neck Surgery, School of Medicine, University of Patras, Patras, 26504, Greece.
| | - Alexander Delides
- 2nd Otolaryngology Department, School of Medicine, National & Kapodistrian University of Athens, 'Attikon' University Hospital, Rimini 1, Athens, 12462, Greece
| | - Efthymios Kyrodimos
- 1st Otolaryngology Department, School of Medicine, National & Kapodistrian University of Athens, 'Ippokrateion' General Hospital, Athens, Greece
| | - Zoi Piperigkou
- Biochemistry, Biochemical Analysis & Matrix Pathobiology Research Group, Laboratory of Biochemistry, Department of Chemistry, University of Patras, Patras, 26504, Greece
| | - Despoina Spyropoulou
- Department of Radiation Oncology, Medical School, University of Patras, Patras, 26504, Greece
| | - Evangelos Giotakis
- 1st Otolaryngology Department, School of Medicine, National & Kapodistrian University of Athens, 'Ippokrateion' General Hospital, Athens, Greece
| | | | - Nikos K Karamanos
- Biochemistry, Biochemical Analysis & Matrix Pathobiology Research Group, Laboratory of Biochemistry, Department of Chemistry, University of Patras, Patras, 26504, Greece
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Rivera-Peña B, Folawiyo O, Turaga N, Rodríguez-Benítez RJ, Felici ME, Aponte-Ortiz JA, Pirini F, Rodríguez-Torres S, Vázquez R, López R, Sidransky D, Guerrero-Preston R, Báez A. Promoter DNA methylation patterns in oral, laryngeal and oropharyngeal anatomical regions are associated with tumor differentiation, nodal involvement and survival. Oncol Lett 2024; 27:89. [PMID: 38268779 PMCID: PMC10804364 DOI: 10.3892/ol.2024.14223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 11/23/2023] [Indexed: 01/26/2024] Open
Abstract
Differentially methylated regions (DMRs) can be used as head and neck squamous cell carcinoma (HNSCC) diagnostic, prognostic and therapeutic targets in precision medicine workflows. DNA from 21 HNSCC and 10 healthy oral tissue samples was hybridized to a genome-wide tiling array to identify DMRs in a discovery cohort. Downstream analyses identified differences in promoter DNA methylation patterns in oral, laryngeal and oropharyngeal anatomical regions associated with tumor differentiation, nodal involvement and survival. Genome-wide DMR analysis showed 2,565 DMRs common to the three subsites. A total of 738 DMRs were unique to laryngeal cancer (n=7), 889 DMRs were unique to oral cavity cancer (n=10) and 363 DMRs were unique to pharyngeal cancer (n=6). Based on the genome-wide analysis and a Gene Ontology analysis, 10 candidate genes were selected to test for prognostic value and association with clinicopathological features. TIMP3 was associated with tumor differentiation in oral cavity cancer (P=0.039), DAPK1 was associated with nodal involvement in pharyngeal cancer (P=0.017) and PAX1 was associated with tumor differentiation in laryngeal cancer (P=0.040). A total of five candidate genes were selected, DAPK1, CDH1, PAX1, CALCA and TIMP3, for a prevalence study in a larger validation cohort: Oral cavity cancer samples (n=42), pharyngeal cancer tissues (n=25) and laryngeal cancer samples (n=52). PAX1 hypermethylation differed across HNSCC anatomic subsites (P=0.029), and was predominantly detected in laryngeal cancer. Kaplan-Meier survival analysis (P=0.043) and Cox regression analysis of overall survival (P=0.001) showed that DAPK1 methylation is associated with better prognosis in HNSCC. The findings of the present study showed that the HNSCC subsites oral cavity, pharynx and larynx display substantial differences in aberrant DNA methylation patterns, which may serve as prognostic biomarkers and therapeutic targets.
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Affiliation(s)
- Bianca Rivera-Peña
- Department of Biology, University of Puerto Rico, San Juan 00925, Puerto Rico
- Department of Pharmacology, University of Puerto Rico School of Medicine, San Juan 00936, Puerto Rico
- Department of Otolaryngology-Head and Neck Surgery, University of Puerto Rico School of Medicine, San Juan 00936, Puerto Rico
| | - Oluwasina Folawiyo
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Nitesh Turaga
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Rosa J. Rodríguez-Benítez
- Department of General Social Sciences, Faculty of Social Sciences, University of Puerto Rico, San Juan 00925, Puerto Rico
| | - Marcos E. Felici
- Oral Health Division, Puerto Rico Department of Health, San Juan 00927, Puerto Rico
| | - Jaime A. Aponte-Ortiz
- Department of General Surgery, University of Puerto Rico School of Medicine, San Juan 00936, Puerto Rico
| | - Francesca Pirini
- Biosciences Laboratory, IRCCS Instituto Romagnolo per lo Studio dei Tumori ‘Dino Amadori’, Meldola I-47014, Italy
| | | | - Roger Vázquez
- Department of Biology, University of Puerto Rico, San Juan 00925, Puerto Rico
| | - Ricardo López
- Department of Biology, University of Puerto Rico, San Juan 00925, Puerto Rico
| | - David Sidransky
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Rafael Guerrero-Preston
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Research and Development, LifeGene-Biomarks, San Juan 00909, Puerto Rico
| | - Adriana Báez
- Department of Pharmacology, University of Puerto Rico School of Medicine, San Juan 00936, Puerto Rico
- Department of Otolaryngology-Head and Neck Surgery, University of Puerto Rico School of Medicine, San Juan 00936, Puerto Rico
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Wang W, Ruan S, Xie Y, Fang S, Yang J, Li X, Zhang Y. Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer. Clin Exp Otorhinolaryngol 2024; 17:85-97. [PMID: 38246983 PMCID: PMC10933807 DOI: 10.21053/ceo.2023.00026] [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: 10/16/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVES The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics, which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8. METHODS Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin-stained images, and then the minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied, followed by screening with the logistic regression algorithm. RESULTS Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model's high clinical applicability. CONCLUSION The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.
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Affiliation(s)
- Weihua Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Suyu Ruan
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yuhang Xie
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shengjian Fang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Junxian Yang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xueyan Li
- Department of Nursing, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yu Zhang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
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Li J, Wang D, Zhang C. Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer. Front Oncol 2024; 13:1232192. [PMID: 38260829 PMCID: PMC10802857 DOI: 10.3389/fonc.2023.1232192] [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: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 01/24/2024] Open
Abstract
CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.
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Affiliation(s)
- Jia Li
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Dongxu Wang
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Chenxin Zhang
- Department of General Surgery, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
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Wang JM, Hong R, Demicco EG, Tan J, Lazcano R, Moreira AL, Li Y, Calinawan A, Razavian N, Schraink T, Gillette MA, Omenn GS, An E, Rodriguez H, Tsirigos A, Ruggles KV, Ding L, Robles AI, Mani DR, Rodland KD, Lazar AJ, Liu W, Fenyö D. Deep learning integrates histopathology and proteogenomics at a pan-cancer level. Cell Rep Med 2023; 4:101173. [PMID: 37582371 PMCID: PMC10518635 DOI: 10.1016/j.xcrm.2023.101173] [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: 07/10/2022] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 08/17/2023]
Abstract
We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.
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Affiliation(s)
- Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Elizabeth G Demicco
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital and Laboratory Medicine and Pathobiology, University of Toronto, Toronto M5G 1X5, ON, Canada
| | - Jimin Tan
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Rossana Lazcano
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Andre L Moreira
- Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Anna Calinawan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Narges Razavian
- Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Tobias Schraink
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Michael A Gillette
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Massachusetts General Hospital Division of Pulmonary and Critical Care Medicine, Boston, MA 02114, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Aristotelis Tsirigos
- Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Kelly V Ruggles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Li Ding
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - D R Mani
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA
| | - Alexander J Lazar
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
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Huang W, Tan K, Zhang Z, Hu J, Dong S. A Review of Fusion Methods for Omics and Imaging Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:74-93. [PMID: 35044920 DOI: 10.1109/tcbb.2022.3143900] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.
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Elmakaty I, Elmarasi M, Amarah A, Abdo R, Malki MI. Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis. Crit Rev Oncol Hematol 2022; 178:103777. [PMID: 35931404 DOI: 10.1016/j.critrevonc.2022.103777] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/21/2022] [Accepted: 08/01/2022] [Indexed: 10/16/2022] Open
Abstract
Oral Squamous Cell Carcinoma (OSCC) is an aggressive tumor with a poor prognosis. Accurate and timely diagnosis is therefore essential for reducing the burden of advanced disease and improving outcomes. In this meta-analysis, we evaluated the accuracy of artificial intelligence (AI)-assisted technologies in detecting OSCC. We included studies that validated any diagnostic modality that used AI to detect OSCC. A search was performed in six databases: PubMed, Embase, Scopus, Cochrane Library, ProQuest, and Web of Science up to 15 Mar 2022. The Quality Assessment Tool for Diagnostic Accuracy Studies was used to evaluate the included studies' quality, while the Split Component Synthesis method was utilized to quantitatively synthesize the pooled diagnostic efficacy estimates. We considered 16 out of the 566 yielded studies, which included twelve different AI models with a total of 6606 samples. The summary sensitivity, summary specificity, positive and negative likelihood ratios as well as the pooled diagnostic odds ratio were 92.0 % (95 % confidence interval [CI] 86.7-95.4 %), 91.9 % (95 % CI 86.5-95.3 %), 11.4 (95 % CI 6.74-19.2), 0.087 (95 % CI 0.051-0.146) and 132 (95 % CI 62.6-277), respectively. Our findings support the capability of AI-assisted systems to detect OSCC with high accuracy, potentially aiding the histopathological examination in early diagnosis, yet more prospective studies are needed to justify their use in the real population.
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Affiliation(s)
| | | | - Ahmed Amarah
- College of Medicine, QU Health, Qatar University, Doha, Qatar.
| | - Ruba Abdo
- College of Medicine, QU Health, Qatar University, Doha, Qatar.
| | - Mohammed Imad Malki
- Pathology Unit, Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, Qatar.
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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Events prediction after treatment in HPV-driven oropharyngeal carcinoma using machine learning. Eur J Cancer 2022; 171:106-113. [PMID: 35714450 DOI: 10.1016/j.ejca.2022.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/21/2022] [Accepted: 05/01/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Our objective was to develop a predictive model using a machine learning signature to identify patients at high risk of relapse or death after treatment for HPV-positive oropharyngeal carcinoma. MATERIALS AND METHODS Pre-treatment variables of 450 patients with HPV-positive oropharyngeal carcinoma treated with a curative intent comprised clinical items, imaging parameters and histological findings. The events considered were progression or residual disease after treatment, the recurrent disease after a disease-free interval and death. The endpoints were the prediction of events and progression-free survival. After feature Z-score normalisation and selection, random forest classifier models were trained. The best models were evaluated on recall, the F-score, and the ROC AUC metric. The clinical relevance of the best prediction model was evaluated using Kaplan-Meier analysis with a log-rank test. RESULTS The best random forest model predicted the 5-year risk of relapse-free survival with a recall of 79.1%, an F1-score of 81.08%, and an AUC of the ROC curve of 0.89. The models performed poorly for the prediction of specific events of progression only, recurrence only or death only. The clinical relevance of the model was validated with a 5-year relapse-free survival of high-risk patients versus low-risk patients of 23.5% and 80%, respectively (p < 0.0001). CONCLUSION Patients with HPV-driven oropharyngeal carcinoma at high risk of relapse-free survival could be identified with a predictive machine learning model using patient data before treatment.
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