Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computa-tional as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles

Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer

Introduction: Colorectal cancer is one of the most frequently diagnosed malignancies and a common cause of cancer-related mortality. The aim of this study was to develop and validate a clinical predictive model for 1-year mortality among patients with colon cancer who survive for at least 30 days after surgery. Methods: Patients diagnosed with colon cancer who had surgery for the first time and who survived 30 days after the surgery were selected prospectively. The outcome was mortality within 1 year. Random forest, genetic algorithms and classification and regression trees were combined in order to identify the variables and partition points that optimally classify patients by risk of mortality. The resulting decision tree was categorized into four risk categories. Split-sample and bootstrap validation were performed. ClinicalTrials.gov Identifier: NCT02488161. Results: A total of 1945 patients were enrolled in the study. The variables identified as the main predictors of 1-year mortality were presence of residual tumor, American Society of Anesthesiologists Physical Status Classification System risk score, pathologic tumor staging, Charlson Comorbidity Index, intraoperative complications, adjuvant chemotherapy and recurrence of tumor. The model was internally validated; area under the receiver operating characteristic curve (AUC) was 0.896 in the derivation sample and 0.835 in the validation sample. Risk categorization leads to AUC values of 0.875 and 0.832 in the derivation and validation samples, respectively. Optimal cut-off point of estimated risk had a sensitivity of 0.889 and a specificity of 0.758. Conclusion: The decision tree was a simple, interpretable, valid and accurate prediction rule of 1-year mortality among colon cancer patients who survived for at least 30 days after surgery.We are grateful for the support of the 22 participating hospitals, as well as the clinicians and staff members of the various services, research, quality units and medical records sections of these hospitals. We also gratefully acknowledge the patients who participated in the study. We would like to thank Editage (www.editage.com) for English language editing. We also wish to thank the anonymous referees for providing comments, which led to substantial improvement of the article. Financial support for this study was provided, in part, by grants from the Instituto de Salud Carlos III (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397 and the thematic network REDISSEC - Red de Investigacion en Servicios de Salud en Enfermedades Cronicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF "Investing in your future"); the Research Committee of the Hospital Galdakao; the Department of Health and the Department of Education, Language Policy and Culture from the Basque Government (2010111098, IT620-13 and BERC 2014-2017 program); the Spanish Ministry of Economy and Competitiveness MINECO and FEDER (MTM2013-40941-P, MTM2016-74931-P and BCAM Severo Ochoa excellence accreditation SEV-2013-0323). The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing and publishing the report

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data

Background: During the last decade, the interest to apply machine learning algorithms to genomic data has increased in many bioinformatics applications. Analyzing this type of data entails difficulties for managing high-dimensional data, class imbalance for knowledge extraction, identifying important features and classifying individuals. In this study, we propose a general framework to tackle these challenges with different machine learning algorithms and techniques. We apply the configuration of this framework on lung cancer patients, identifying genetic signatures for classifying response to drug treatment response. We intersect these relevant SNPs with the GWAS Catalog of the National Human Genome Research Institute and explore the Regulomedb, GTEx databases for functional analysis purposes. Results: The machine learning based solution proposed in this study is a scalable and flexible alternative to the classical uni-variate regression approach to analyze large-scale data. From 36 experiments executed using the machine learning framework design, we obtain good classification performance from the top 5 models with the highest cross-validation score and the smallest standard deviation. One thousand two hundred twenty four SNPs corresponding to the key features from the top 20 models (cross validation F1 mean >= 0.65) were compared with the GWAS Catalog finding no intersection with genome-wide significant reported hits. From these, new genetic signatures in MAE, CEP104, PRKCZ and ADRB2 show relevant biological regulatory functionality related to lung physiology. Conclusions: We have defined a machine learning framework using data with an unbalanced large data-set of SNP-arrays and imputed genotyping data from a pharmacogenomics study in lung cancer patients subjected to first-line platinum-based treatment. This approach found genome signals with no genome-wide significance in the uni-variate regression approach (GWAS Catalog) that are valuable for classifying patients, only few of them with related biological function. The effect results of these variants can be explained by the recently proposed omnigenic model hypothesis, which states that complex traits can be influenced mostly by genes outside not only by the “core genes”, mainly found by the genome-wide significant SNPs, but also by the rest of genes outside of the “core pathways” with apparent unrelated biological functionality.Peer ReviewedPostprint (published version

Critical research gaps and recommendations to inform research prioritisation for more effective prevention and improved outcomes in colorectal cancer

OBJECTIVE: Colorectal cancer (CRC) leads to significant morbidity/mortality worldwide. Defining critical research gaps (RG), their prioritisation and resolution, could improve patient outcomes.DESIGN: RG analysis was conducted by a multidisciplinary panel of patients, clinicians and researchers (n=71). Eight working groups (WG) were constituted: discovery science; risk; prevention; early diagnosis and screening; pathology; curative treatment; stage IV disease; and living with and beyond CRC. A series of discussions led to development of draft papers by each WG, which were evaluated by a 20-strong patient panel. A final list of RGs and research recommendations (RR) was endorsed by all participants.RESULTS: Fifteen critical RGs are summarised below: RG1: Lack of realistic models that recapitulate tumour/tumour micro/macroenvironment; RG2: Insufficient evidence on precise contributions of genetic/environmental/lifestyle factors to CRC risk; RG3: Pressing need for prevention trials; RG4: Lack of integration of different prevention approaches; RG5: Lack of optimal strategies for CRC screening; RG6: Lack of effective triage systems for invasive investigations; RG7: Imprecise pathological assessment of CRC; RG8: Lack of qualified personnel in genomics, data sciences and digital pathology; RG9: Inadequate assessment/communication of risk, benefit and uncertainty of treatment choices; RG10: Need for novel technologies/interventions to improve curative outcomes; RG11: Lack of approaches that recognise molecular interplay between metastasising tumours and their microenvironment; RG12: Lack of reliable biomarkers to guide stage IV treatment; RG13: Need to increase understanding of health related quality of life (HRQOL) and promote residual symptom resolution; RG14: Lack of coordination of CRC research/funding; RG15: Lack of effective communication between relevant stakeholders.CONCLUSION: Prioritising research activity and funding could have a significant impact on reducing CRC disease burden over the next 5 years.</p

Sex Difference In Identification Of Predictive Tumor Tissue Metabolites Associated With Colorectal Cancer Prognosis

Colorectal cancer (CRC) is the third major cause of cancer-related deaths in the United States in 2020. Sex-related differences in CRC stage, prognosis, and metabolism have become increasingly popular in cancer research. Males have poorer survival for CRC, but females with right-sided colon cancer (RCC) have aberrant metabolism correlated with poor survival. Delay in knowing the condition of CRC in female patients would result in poor prognosis, which could be avoided by predicting prognostic outcomes. Random Survival Forest (RSF) is ideal for exploration and making predictions using metabolomics data with high dimension, strong collinearity, and heterogeneity, which CPH models could not efficiently address. In this retrospective study including 197 patients, we applied an RSF prediction method based on the backward selection algorithm in 5-year overall survival (OS) for 95 female CRC patients and validated its performance. We also investigated Cox proportional hazard models (CPH), lasso penalized Cox regression (Cox-Lasso), and Logistic Regression (LR) and compared their predictive performances. RSF using the backward selection algorithm showed the best performance with the C-index of the training and testing sets reaching 0.81(95% CI: 0.810-0.813) and 0.78 (95% CI: 0.776-0.777) respectively and identified the five most predictive metabolites for female 5-year OS: glutathione, citrulline, phosphoenolpyruvate, lysoPC (16:0), and asparagine. Accordingly, the backward selection algorithm-based Random Survival Forest model using tumor tissue metabolic profile is promising for predicting 5-year OS for female CRC patients. The results could be easily interpreted and applied in preventive medicine and precision medicine, guiding clinicians in choosing targeted treatments by sex for better survival and avoiding unnecessary treatments

Combining statistical techniques to predict post-surgical risk of 1-year mortality for patients with colon cancer

Introduction: Colorectal cancer is one of the most frequently diagnosed malignancies and a common cause of cancer-related mortality. The aim of this study was to develop and validate a clinical predictive model for1-year mortality among patients with colon cancer who survive for at least 30 days after surgery. Methods: Patients diagnosed with colon cancer who had surgery for the first time and who survived 30 days after the surgery were selected prospectively. The outcome was mortality within 1 year. Random forest, genetic algorithms and classification and regression trees were combined in order to identify the variables and partition points that optimally classify patients by risk of mortality. The resulting decision tree was categorized into four risk categories. Split-sample and bootstrap validation were performed. Results: A total of 1945 patients were enrolled in the study. The variables identified as the main predictors of 1-year mortality were presence of residual tumour, ASA risk score, pathological tumour staging, Charlson comorbidity index, intraoperative complications, adjuvant chemotherapy and recurrence of tumour. The model was internally validated; the area under the curve (AUC) was 0.896 in the derivation sample and 0.835 in the validation sample. Risk categorization leads to AUC values of 0.875 and 0.832 in the derivation and validation samples, respectively. Optimal cut-off point of estimated risk had a sensitivity of 0.889 and a specificity of 0.758. Conclusions: The decision-tree was a simple, interpretable, valid and accurate prediction rule of 1-year mortality among colon cancer patients who survived for at least 30 days after surgery.Instituto de Salud Carlos III (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397 and the thematic networks REDISSEC - Red de Investigación en Servicios de Salud en Enfermedades Crónicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF "Investing in your future"); Research Committee of the Hospital Galdakao Department of Health and the Department of Education, Language Policy and Culture from the Basque Government (2010111098, IT620-13) MINECO and FEDER (MTM2013-40941-P, MTM2016-74931-P)