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Stowers CE, Wu C, Xu Z, Kumar S, Yam C, Son JB, Ma J, Tamir JI, Rauch GM, Yankeelov TE. Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. Radiol Artif Intell 2025; 7:e240124. [PMID: 39503605 DOI: 10.1148/ryai.240124] [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: 11/08/2024]
Abstract
Purpose To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN predictions of the calibrated model parameters were used to estimate tumor response at the end of NAC. CNN performance in the estimations of total tumor volume (TTV), total tumor cellularity (TTC), and tumor status was evaluated. Model-predicted TTC and TTV measurements were compared with MRI-based measurements using the concordance correlation coefficient and area under the receiver operating characteristic curve (for predicting pathologic complete response at the end of NAC). Results The study included 118 female patients (median age, 51 years [range, 29-78 years]). For comparison of CNN predicted to measured change in TTC and TTV over the course of NAC, the concordance correlation coefficient values were 0.95 (95% CI: 0.90, 0.98) and 0.94 (95% CI: 0.87, 0.97), respectively. CNN-predicted TTC and TTV had an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.34, 0.94) and 0.72 (95% CI: 0.40, 0.95) for predicting tumor status at the time of surgery, respectively. Conclusion Deep learning integrated with a biology-based mathematical model showed good performance in predicting the spatial and temporal evolution of a patient's tumor during NAC using only pre-NAC MRI data. Keywords: Triple-Negative Breast Cancer, Neoadjuvant Chemotherapy, Convolutional Neural Network, Biology-based Mathematical Model Supplemental material is available for this article. Clinical trial registration no. NCT02276443 ©RSNA, 2024 See also commentary by Mei and Huang in this issue.
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Affiliation(s)
- Casey E Stowers
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Chengyue Wu
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Zhan Xu
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Sidharth Kumar
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Clinton Yam
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Jong Bum Son
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Jingfei Ma
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Jonathan I Tamir
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Gaiane M Rauch
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
| | - Thomas E Yankeelov
- From the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712
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Zhan F, Guo Y, He L. NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01366-6. [PMID: 39663319 DOI: 10.1007/s10278-024-01366-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Revised: 11/15/2024] [Accepted: 11/29/2024] [Indexed: 12/13/2024]
Abstract
To evaluate the prognostic significance and molecular mechanism of NETosis markers in ovarian serous cystadenocarcinoma (OSC), we constructed a machine learning-based pathomic model utilizing hematoxylin and eosin (H&E) slides. We analyzed 333 patients with OSC from The Cancer Genome Atlas for prognostic-related neutrophil extracellular trap formation (NETosis) genes through bioinformatics analysis. Pathomic features were extracted from 54 cases with complete pathological images, genetic matrices, and clinical information. Two pathomic prognostic models were constructed using support vector machine (SVM) and logistic regression (LR) algorithms. Additionally, we established a predictive scoring system that integrated pathomic scores based on the NETcluster subtypes and clinical signature. We identified four NETosis genes significantly correlated with OSC prognosis, which were functionally associated with immune response, somatic mutations, tumor invasion, and metastasis. Five robust pathomic features were selected for overall survival prediction. The LR and SVM pathomic models demonstrated strong predictive performance for the NETcluster subtype classification through five-fold cross-validation. Time-dependent ROC analysis revealed excellent prognostic capability of the LR pathomic model's score for the overall survival (AUC values of 0.658, 0.761, and 0.735 at 36, 48, and 60 months, respectively), further validated by Kaplan-Meier analysis. The expression levels of NETosis genes greatly affected OSC patients' prognoses. The pathomic analysis of H&E slide pathological images provides an effective approach for predicting both NETcluster subtype and overall survival in OSC patients.
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Affiliation(s)
- Feng Zhan
- College of Engineering, Fujian Jiangxia University, Fuzhou, Fujian, China
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China
| | - Yina Guo
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China
| | - Lidan He
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
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Han G, Liu X, Gao T, Zhang L, Zhang X, Wei X, Lin Y, Yin B. Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics. Mol Cell Probes 2024; 78:101983. [PMID: 39299554 DOI: 10.1016/j.mcp.2024.101983] [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: 07/15/2024] [Revised: 09/15/2024] [Accepted: 09/16/2024] [Indexed: 09/22/2024]
Abstract
AIM In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics. METHODS Transcriptome data, pathological images, and clinical data from 443 cases were retrieved from TCGA (The Cancer Genome Atlas Program) for survival analysis. The images were segmented using the Otsu algorithm, and features were extracted using the PyRadiomics package. Subsequently, the cases were randomly divided into a training cohort of 165 cases and a validation cohort of 69 cases. Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves. Additionally, the correlation between the Pathomics score (PS) and immune genes was examined. RESULTS In the multivariate analysis, heightened infiltration of activated CD4 memory T cells was strongly associated with improved overall survival (HR = 0.505, 95 % CI = 0.342-0.745, P < 0.001). The pathomic model, exhibiting robust predictive capability, demonstrated impressive AUC values of 0.844 and 0.750 in both study cohorts. The Decision Curve Analysis (DCA) unequivocally underscored the model's exceptional clinical utility. In a subsequent multivariate analysis, heightened infiltration of the PS also emerged as a significant protective factor for overall survival (HR = 0.506, 95 % CI = 0.329-0.777, P = 0.002). CONCLUSION The pathomic model based on H&E slides for predicting the infiltration degree of activated CD4 memory T cells, along with integrated bioinformatics analysis elucidating potential molecular mechanisms, offers novel prognostic indicators for the precise stratification and individualized prognosis of gastric cancer patients.
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Affiliation(s)
- Guoda Han
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China.
| | - Xu Liu
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Tian Gao
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Lei Zhang
- Department of Clinical Laboratory, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Xiaoling Zhang
- Pathology Department, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Xiaonan Wei
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Yecheng Lin
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Bohong Yin
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
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Li Y, Pan L, Mugaanyi J, Li H, Li G, Huang J, Dai L. Pathomic and bioinformatics analysis of clinical-pathological and genomic factors for pancreatic cancer prognosis. Sci Rep 2024; 14:27769. [PMID: 39533091 PMCID: PMC11557977 DOI: 10.1038/s41598-024-79619-1] [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: 03/26/2024] [Accepted: 11/11/2024] [Indexed: 11/16/2024] Open
Abstract
Pancreatic cancer exhibits a high degree of malignancy with a poor prognosis, lacking effective prognostic targets. Utilizing histopathological methodologies, this study endeavors to predict the expression of pathological features in pancreatic ductal adenocarcinoma (PAAD) and investigate their underlying molecular mechanisms. Pathological images, transcriptomic, and clinical data from TCGA-PAAD were collected for survival analysis. Image segmentation using unsupervised machine learning was employed to extract features, perform clustering, and establish models. The prognostic value of pathological features and associated clinical risk factors were evaluated; the correlation between pathological features and molecular mechanisms, gene mutations, and immune infiltration was analyzed. By clustering 45 effective pathological features, we divided PAAD patients into two groups: cluster 1 and cluster 2. Significant associations with poor prognosis were found for cluster 2 in both the training group (n = 113) and validation group (n = 75) (p = 0.006), with pathological stages II-IV identified as potential synergistic risk factors (HR = 2.421, 95% CI = 1.263-4.639, p = 0.008). Subsequently, through multi-omics correlation analysis, we further revealed a close association between cluster 2 and the oxidative phosphorylation mechanism. Within the cluster 2 group, 28 oxidative phosphorylation genes exhibited reduced expression, CDKN2A gene mutations were upregulated, and there was significant downregulation of Tregs infiltration and related immune gene expression. The pathomic model constructed using machine learning serves as a valuable prognostic target for PAAD. The histopathological features cluster 2 are closely associated with the downregulation of oxidative phosphorylation levels and Tregs immune infiltration.
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Affiliation(s)
- Yang Li
- Department of Emergency, Ningbo Medical Centre Lihuili Hospital, The affiliated hospital of Ningbo University, Ningbo, 315040, Zhejiang, China
| | - Lujuan Pan
- Department of Gastroenterology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China
- Key Laboratory of Tumor Molecular Pathology of Baise, Baise, 533000, Guangxi, China
| | - Joseph Mugaanyi
- Department of Hepato-pancreato-biliary Surgery, Ningbo Medical Centre Lihuili Hospital, The affiliated hospital of Ningbo University, Ningbo, 315040, Zhejiang, China
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Hua Li
- Key Laboratory of Tumor Molecular Pathology of Baise, Baise, 533000, Guangxi, China
| | - Gehui Li
- Department of Hepato-pancreato-biliary Surgery, Ningbo Medical Centre Lihuili Hospital, The affiliated hospital of Ningbo University, Ningbo, 315040, Zhejiang, China
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Jing Huang
- Department of Hepato-pancreato-biliary Surgery, Ningbo Medical Centre Lihuili Hospital, The affiliated hospital of Ningbo University, Ningbo, 315040, Zhejiang, China.
- Department of Hepato-pancreato-biliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, China.
| | - Lei Dai
- Department of Hepato-pancreato-biliary Surgery, Ningbo Medical Centre Lihuili Hospital, The affiliated hospital of Ningbo University, Ningbo, 315040, Zhejiang, China.
- Department of Hepato-pancreato-biliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, China.
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Zhou X, Man M, Cui M, Zhou X, Hu Y, Liu Q, Deng Y. Relationship between EZH2 expression and prognosis of patients with hepatocellular carcinoma using a pathomics predictive model. Heliyon 2024; 10:e38562. [PMID: 39640777 PMCID: PMC11619983 DOI: 10.1016/j.heliyon.2024.e38562] [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: 04/02/2024] [Revised: 09/04/2024] [Accepted: 09/26/2024] [Indexed: 12/07/2024] Open
Abstract
Background Enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) is overexpressed in hepatocellular carcinoma, promoting tumorigenesis and correlating with poor prognosis. Traditional histopathological examinations are insufficient to accurately predict hepatocellular carcinoma (HCC) survival; however, pathomics models can predict EZH2 expression and HCC prognosis. This study aimed to investigate the relationship between pathomics features and EZH2 expression for predicting overall survival of patients with HCC. Methods We analyzed 267 patients with HCC from the Cancer Genome Atlas database, with available pathological images and gene expression data. RNA sequencing data were divided into high and low EZH2 expression groups for prognosis and survival analysis. Pathological image features were screened using mRMR_RFE. A pathological model was constructed using a gradient boosting machine (GBM) algorithm, and efficiency evaluation and survival analysis of the model were performed. The R package "survminer" took the pathomics score (PS) cutoff value of 0.4628 to divide the patients into two groups: high and low PS expression. Survival analyses included Kaplan-Meier curve analysis, univariate and multivariate Cox regression analyses, and interaction tests. Potential pathomechanisms were explored through enrichment, differential, immune cell infiltration abundance, and gene mutation analyses. Result EZH2 was highly expressed in tumor samples but poorly expressed in normal tissue samples. Univariate and multivariate Cox regression analyses revealed that EZH2 was an independent risk factor for HCC (hazard ratio [HR], 2.792 and 3.042, respectively). Seven imaging features were selected to construct a pathomics model to predict EZH2. Decision curve analysis showed that the model had high clinical utility. Multivariate Cox regression analysis showed that high PS expression was an independent risk factor for HCC prognosis (HR, 2.446). The Kaplan-Meier curve showed that high PS expression was a risk factor for overall survival. Conclusion EZH2 expression can affect the prognosis of patients with liver cancer. Our pathological model could predict EZH2 expression and prognosis of patients with HCC with high accuracy and robustness, making it a new and potentially valuable tool.
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Affiliation(s)
- Xulin Zhou
- Department of Oncology, Hefei BOE Hospital, Hefei, PR China
| | - Muran Man
- Department of Oncology, People's Hospital of Shizhong District, Zaozhuang City, Shandong Province, PR China
| | - Min Cui
- Affiliated Hospital Of Jining Medical University (Shanxian Central Hospital), Heze City, Shandong Province, PR China
| | - Xiang Zhou
- People's Hospital of Xinjiang Uygur Autonomous Region Urumqi, Xinjiang, CN, PR China
| | - Yan Hu
- Department of Oncology, Hefei BOE Hospital, Hefei, PR China
| | - Qinghua Liu
- Department of Oncology, Deyang People's Hospital, Deyang, Sichuan, CN, PR China
| | - Youxing Deng
- Department of Oncology, Hefei BOE Hospital, Hefei, PR China
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Hou J, Yi X, Li H, Lu Q, Lin H, Li J, Zeng B, Yu X. Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma. J Cancer Res Clin Oncol 2024; 150:415. [PMID: 39249584 PMCID: PMC11384600 DOI: 10.1007/s00432-024-05930-z] [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: 04/04/2024] [Accepted: 08/21/2024] [Indexed: 09/10/2024]
Abstract
PURPOSE To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients. METHODS 357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test. RESULTS The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001). CONCLUSION The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.
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Affiliation(s)
- Jing Hou
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China
| | - Xiaochun Yi
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China
| | - Handong Li
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China
| | - Qiang Lu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China
| | - Huashan Lin
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, P. R. China
| | - Junjun Li
- Department of Pathology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China.
| | - Biao Zeng
- Department of Radiotherapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China.
| | - Xiaoping Yu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China.
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Thomas N, Garaud S, Langouo M, Sofronii D, Boisson A, De Wind A, Duwel V, Craciun L, Larsimont D, Awada A, Willard-Gallo K. Tumor-Infiltrating Lymphocyte Scoring in Neoadjuvant-Treated Breast Cancer. Cancers (Basel) 2024; 16:2895. [PMID: 39199667 PMCID: PMC11352458 DOI: 10.3390/cancers16162895] [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: 07/10/2024] [Revised: 08/13/2024] [Accepted: 08/14/2024] [Indexed: 09/01/2024] Open
Abstract
Neoadjuvant chemotherapy (NAC) is now the standard of care for patients with locally advanced breast cancer (BC). TIL scoring is prognostic and adds predictive value to the residual cancer burden evaluation after NAC. However, NAC induces changes in the tumor, and the reliability of TIL scoring in post-NAC samples has not yet been studied. H&E- and dual CD3/CD20 chromogenic IHC-stained tissues were scored for stromal and intra-tumoral TIL by two experienced pathologists on pre- and post-treatment BC tissues. Digital TIL scoring was performed using the HALO® image analysis software (version 2.2). In patients with residual disease, we show a good inter-pathologist correlation for stromal TIL on H&E-stained tissues (CCC value 0.73). A good correlation for scoring with both staining methods (CCC 0.81) and the digital TIL scoring (CCC 0.77) was also observed. Overall concordance for TIL scoring in patients with a complete response was however poor. This study reveals there is good reliability for TIL scoring in patients with detectable residual tumors after NAC treatment, which is comparable to the scoring of untreated breast cancer patients. Based on the good consistency observed with digital TIL scoring, the development of a validated algorithm in the future might be advantageous.
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Affiliation(s)
- Noémie Thomas
- Molecular Immunology Unit, Institut Jules Bordet, 1070 Brussels, Belgium (A.B.)
| | - Soizic Garaud
- Molecular Immunology Unit, Institut Jules Bordet, 1070 Brussels, Belgium (A.B.)
| | - Mireille Langouo
- Molecular Immunology Unit, Institut Jules Bordet, 1070 Brussels, Belgium (A.B.)
| | - Doïna Sofronii
- Molecular Immunology Unit, Institut Jules Bordet, 1070 Brussels, Belgium (A.B.)
| | - Anaïs Boisson
- Molecular Immunology Unit, Institut Jules Bordet, 1070 Brussels, Belgium (A.B.)
| | - Alexandre De Wind
- Anantomical Pathology Department, Institut Jules Bordet, 1070 Brussels, Belgium
| | - Valérie Duwel
- Anatomical Pathology Department, AZ Klina, 2930 Brasschaat, Belgium;
| | - Ligia Craciun
- Anantomical Pathology Department, Institut Jules Bordet, 1070 Brussels, Belgium
- Tumor Bank, Institut Jules Bordet, 1070 Brussels, Belgium
| | - Dennis Larsimont
- Anantomical Pathology Department, Institut Jules Bordet, 1070 Brussels, Belgium
| | - Ahmad Awada
- Medical Oncology, Institut Jules Bordet, 1070 Brussels, Belgium
| | - Karen Willard-Gallo
- Molecular Immunology Unit, Institut Jules Bordet, 1070 Brussels, Belgium (A.B.)
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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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9
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Hu D, Jiang Z, Shi J, Xie F, Wu K, Tang K, Cao M, Huai J, Zheng Y. Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval. Med Image Anal 2024; 95:103163. [PMID: 38626665 DOI: 10.1016/j.media.2024.103163] [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: 05/30/2023] [Revised: 03/09/2024] [Accepted: 04/02/2024] [Indexed: 04/18/2024]
Abstract
Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI. An anchor-based WSI encoder is built to extract hierarchical region features and a prompt-based text encoder is introduced to learn fine-grained semantics from the diagnosis reports. The proposed framework is trained with a multivariate cross-modal loss function to learn semantic information from the diagnosis report at both the instance level and region level. After training, it can perform four types of retrieval tasks based on the multi-modal database to support diagnostic requirements. We conducted experiments on an in-house dataset and a public dataset to evaluate the proposed method. Extensive experiments have demonstrated the effectiveness of the proposed method and its advantages to the present histopathology retrieval methods. The code is available at https://github.com/hudingyi/FGCR.
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Affiliation(s)
- Dingyi Hu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, 100191, China; Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China
| | - Zhiguo Jiang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, 100191, China; Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China
| | - Jun Shi
- School of Software, Hefei University of Technology, Hefei 230601, China
| | - Fengying Xie
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, 100191, China; Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China
| | - Kun Wu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, 100191, China; Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China
| | - Kunming Tang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, 100191, China; Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China
| | - Ming Cao
- Department of Pathology, the First People's Hospital of Wuhu, China
| | - Jianguo Huai
- Department of Pathology, the First People's Hospital of Wuhu, China
| | - Yushan Zheng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, 100191, China.
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10
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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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11
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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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12
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McCaffrey C, Jahangir C, Murphy C, Burke C, Gallagher WM, Rahman A. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn 2024; 24:363-377. [PMID: 38655907 DOI: 10.1080/14737159.2024.2346545] [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: 12/07/2023] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.
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Affiliation(s)
- Christine McCaffrey
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Chowdhury Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
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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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Dell'Aquila K, Vadlamani A, Maldjian T, Fineberg S, Eligulashvili A, Chung J, Adam R, Hodges L, Hou W, Makower D, Duong TQ. Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population. Breast Cancer Res 2024; 26:7. [PMID: 38200586 PMCID: PMC10782738 DOI: 10.1186/s13058-023-01762-w] [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: 09/23/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population. METHODS Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation. RESULTS pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05). CONCLUSION Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.
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Affiliation(s)
- Kevin Dell'Aquila
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Abhinav Vadlamani
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Takouhie Maldjian
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Susan Fineberg
- Department of Pathology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Anna Eligulashvili
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Julie Chung
- Department of Oncology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Richard Adam
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Laura Hodges
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Wei Hou
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Della Makower
- Department of Oncology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA.
- Center for Health Data Innovation, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA.
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Li X, Yang X, Yang X, Xie X, Rui W, He H. Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma. Technol Cancer Res Treat 2024; 23:15330338241307686. [PMID: 39703069 DOI: 10.1177/15330338241307686] [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: 12/21/2024] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and treatment strategies for ccRCC. This study aims to create a pathomics model to predict OS in ccRCC patients. In this study, data from ccRCC patients in the TCGA database were used as a training set, with clinical data serving as a validation set. Pathological features were extracted from H&E-stained slides using PyRadiomics, and a pathomics model was constructed using the non-negative matrix factorization (NMF) algorithm. The model's predictive performance was assessed through Kaplan-Meier (KM) survival curves and Cox regression analysis. Additionally, differential gene expression, gene ontology (GO) enrichment analysis, immune infiltration, and mutational analysis were conducted to investigate the underlying biological mechanisms. A total of 368 pathomics features were extracted from H&E-stained slides of ccRCC patients, and a pathomics model comprising two subtypes (Cluster 1 and Cluster 2) was successfully constructed using the NMF algorithm. KM survival curves and Cox regression analysis revealed that Cluster 2 was associated with worse OS. A total of 76 differential genes were identified between the two subtypes, primarily involving extracellular matrix organization and structure. Immune-related genes, including CTLA4, CD80, and TIGIT, were highly expressed in Cluster 2, while the VHL and PBRM1 genes, along with mutations in the PI3K-Akt, HIF-1, and MAPK signaling pathways, exhibited mutation rates exceeding 40% in both subtypes. The machine learning-based pathomics model effectively predicts the OS of ccRCC patients and differentiates between subtypes. The critical roles of the immune-related gene CTLA4 and the PI3K-Akt, HIF-1, and MAPK signaling pathways offer new insights for further research on the molecular mechanisms, diagnosis, and treatment strategies for ccRCC.
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Affiliation(s)
- Xiangyun Li
- Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xiaoqun Yang
- Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xianwei Yang
- Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xin Xie
- Department of Urology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wenbin Rui
- Department of Urology, 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, USA
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Aswolinskiy W, Munari E, Horlings HM, Mulder L, Bogina G, Sanders J, Liu YH, van den Belt-Dusebout AW, Tessier L, Balkenhol M, Stegeman M, Hoven J, Wesseling J, van der Laak J, Lips EH, Ciompi F. PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning. Breast Cancer Res 2023; 25:142. [PMID: 37957667 PMCID: PMC10644597 DOI: 10.1186/s13058-023-01726-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/02/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. METHODS In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). RESULTS We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. CONCLUSION The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.
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Affiliation(s)
- Witali Aswolinskiy
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Enrico Munari
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Hugo M Horlings
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Lennart Mulder
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Giuseppe Bogina
- Pathology Unit, IRCCS Sacro Cuore Don Calabria Hospital, Negrar di Valpolicella, Verona, Italy
| | - Joyce Sanders
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Yat-Hee Liu
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | | | - Leslie Tessier
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Center for Integrated Oncology (Institut du cancer de l'Ouest), Angers, France
| | - Maschenka Balkenhol
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michelle Stegeman
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeffrey Hoven
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jelle Wesseling
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Esther H Lips
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
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Nimgaonkar V, Krishna V, Krishna V, Tiu E, Joshi A, Vrabac D, Bhambhvani H, Smith K, Johansen JS, Makawita S, Musher B, Mehta A, Hendifar A, Wainberg Z, Sohal D, Fountzilas C, Singhi A, Rajpurkar P, Collisson EA. Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer. Cell Rep Med 2023; 4:101013. [PMID: 37044094 PMCID: PMC10140610 DOI: 10.1016/j.xcrm.2023.101013] [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: 08/31/2022] [Revised: 12/31/2022] [Accepted: 03/21/2023] [Indexed: 04/14/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.
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Affiliation(s)
| | | | | | - Ekin Tiu
- Valar Labs, Inc., Palo Alto, CA, USA
| | | | | | | | - Katelyn Smith
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Julia S Johansen
- Departments of Oncology and Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Arnav Mehta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Zev Wainberg
- University of California Los Angeles, Los Angeles, CA, USA
| | | | | | - Aatur Singhi
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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