Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues

Tan, Xiao; Su, Andrew; Hajiabadi, Hamideh; Trân, Minh; Nguyen, Quan

doi:10.1007/978-1-0716-0826-5_10

Cited by 14 publications

(4 citation statements)

References 47 publications

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“…Their trained model was able to carry out spatially‐resolved inferences of gene expression relying only on computational analysis of phenotype 79 . These are only a selection of many recent studies that clearly demonstrate the successful application of deep learning to improve our understanding of heterogeneity in cancer 74,77–89 …”

Section: Artificial Intelligence As An Aide In Pathologymentioning

confidence: 99%

“…79 These are only a selection of many recent studies that clearly demonstrate the successful application of deep learning to improve our understanding of heterogeneity in cancer. 74,[77][78][79][80][81][82][83][84][85][86][87][88][89] While impressive when these robust morpho-molecular associations are established with deep learning, it is important to note that in addition to their stochasticity, they require prior knowledge and validation around specific mutations (e.g., FGFR3 mutations in bladder cancer) and may therefore not generalize well outside of their narrow training context and thus be less suited for exploring changes that emerge downstream of these common initiating genetic events and following therapy. 90,91 These approaches will likely therefore fall short for discovery-based research applications.…”

Section: Supervised Computational Pathology Approaches To Resolving T...mentioning

confidence: 99%

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Standardizing analysis of intra‐tumoral heterogeneity with computational pathology

Paliwal

Faust

Alshoumer

et al. 2023

Genes Chromosomes & Cancer

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Many malignant cancers like glioblastoma are highly adaptive diseases that dynamically change their regional biology to survive and thrive under diverse microenvironmental and therapeutic pressures. While the concept of intra‐tumoral heterogeneity has become a major paradigm in cancer research and care, systematic approaches to assess and document bio‐variation in cancer are still in their infancy. Here we discuss existing approaches and challenges to documenting intra‐tumoral heterogeneity and emerging computational approaches that leverage artificial intelligence to begin to overcome these limitations. We propose how these emerging techniques can be coupled with a diversity of molecular tools to address intra‐tumoral heterogeneity more systematically in research and in practice, especially across larger specimens and longitudinal analyses. Systematic documentation and characterization of heterogeneity across entire tumor specimens and their longitudinal evolution has the potential to improve our understanding and treatment of cancer.

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Section: Artificial Intelligence As An Aide In Pathologymentioning

confidence: 99%

Section: Supervised Computational Pathology Approaches To Resolving T...mentioning

confidence: 99%

Standardizing analysis of intra‐tumoral heterogeneity with computational pathology

Paliwal

Faust

Alshoumer

et al. 2023

Genes Chromosomes & Cancer

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“…Last advances in artificial intelligence and machine learning models allowed us to better predict cell subtypes and infer their proportions in tumours and TME based on their inherent multi-omics characteristics. These approaches have the potential to integrate both molecular and histopathological imaging data to refine tumour heterogeneity in the spatial context and go beyond what can be distinguished by routine microscopy observations [90,91]. However, due to their recent development, they still lack standardisation and need further evaluation prior to their implementation in a clinical setting [91].…”

Section: Ith Is Associated With Poorer Clinical Outcomesmentioning

confidence: 99%

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

Gilson

Merlin

Harlé

2022

Cancers

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Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH.

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“…The application of big data to medicine has prompted recent advances in data analytics. The application of artificial intelligence in diagnostics will help doctors improve accuracy in various fields such as medical imaging, bioinformatics, and electronic health records (EHRs) (6)(7)(8)(9). Recently, Pergialiotis et al used artificial neural networks (NNs), and classification and regression trees for the prediction of EC in postmenopausal women in Greece.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method

Wang

Yuan

et al. 2022

Front. Med.

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Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutive patients between November 2012 and January 2021 at tertiary hospitals in central China. Inclusion criteria included women undergoing endometrial biopsy, dilation and curettage, or hysterectomy. A total of 9 features, including patient demographics, vital signs, and laboratory and ultrasound results, were selected in the final analysis. This new model was combined with three top optimal ML methods, namely, logistic regression, gradient-boosted decision tree, and random forest. A total of 1,922 patients were eligible for final analysis and modeling. The ensemble model, called TJHPEC, was validated in an internal validation cohort and two external validation cohorts. The results showed that the AUC values were 0.9346, 0.8341, and 0.8649 for the prediction of total EC and 0.9347, 0.8073, and 0.871 for prediction of stage I EC. Nine clinical features were confirmed to be highly related to the prediction of EC in TJHPEC. In conclusion, our new model may be accurate for identifying EC, especially in the early stage, in the general population of central China.

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Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues

Cited by 14 publications

References 47 publications

Standardizing analysis of intra‐tumoral heterogeneity with computational pathology

Standardizing analysis of intra‐tumoral heterogeneity with computational pathology

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method

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