Deep learning predicts chromosomal instability from histopathology images

Xu, Zhuoran; Verma, Akanksha; Naveed, Uska; Bakhoum, Samuel F.; Khosravi, Pegah; Elemento, Olivier

doi:10.1016/j.isci.2021.102394

Cited by 32 publications

(17 citation statements)

References 37 publications

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“…Xu et al, developed a deep learning model to accurately classify chromosomal instability status on a cohort of 1010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas achieving, an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of chromosomal instability status suggested intra-tumor heterogeneity within slides [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer

Nero

Boldrini

Lenkowicz

et al. 2022

IJMS

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BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.

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Section: Discussionmentioning

confidence: 99%

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer

Nero

Boldrini

Lenkowicz

et al. 2022

IJMS

View full text Add to dashboard Cite

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“…Despite the importance of CIN in breast cancer, it cannot be routinely detected in clinic due to technical shortcomings ( 24 ). Although most studies still detect CIN by traditional detection methods, such as comparative genomic hybridization (CGH), single nucleotide polymorphism (SNP) array, polymerase chain reaction (PCR), and flow cytometry, these methods are not excessively accurate.…”

Section: Methods Of Chromosomal Instability Detectionmentioning

confidence: 99%

The progress in our understanding of CIN in breast cancer research

Liao

Cao²

2023

Front. Oncol.

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Chromosomal instability (CIN) is an important marker of cancer, which is closely related to tumorigenesis, disease progression, treatment efficacy, and patient prognosis. However, due to the limitations of the currently available detection methods, its exact clinical significance remains unknown. Previous studies have demonstrated that 89% of invasive breast cancer cases possess CIN, suggesting that it has potential application in breast cancer diagnosis and treatment. In this review, we describe the two main types of CIN and discuss the associated detection methods. Subsequently, we highlight the impact of CIN in breast cancer development and progression and describe how it can influence treatment and prognosis. The goal of this review is to provide a reference on its mechanism for researchers and clinicians.

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“…The delta-radiomics is proposed as a biomarker to help in predicting cancer treatment outcomes [ 81 ]. Some recent studies reported that ML predicts somatic mutation and chromosomal instability of tumors from images [ 82 , 83 ]. Thus, ML is expected to become more and more important as the development of multimodal characterization of tumor cells continues.…”

Section: Machine Learning—a Keystone That Paves the Way For Precision Oncologymentioning

confidence: 99%

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Baptiste

Moinuddeen

Soliz

et al. 2021

Genes

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Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

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Deep learning predicts chromosomal instability from histopathology images

Cited by 32 publications

References 37 publications

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer

The progress in our understanding of CIN in breast cancer research

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

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