DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

Shirazi, Amin Zadeh; Fornaciari, Eric; Bagherian, Narjes Sadat; Ebert, Lisa M.; Koszyca, Barbara; Gómez, Guillermo A.

doi:10.1007/s11517-020-02147-3

Cited by 41 publications

(34 citation statements)

References 53 publications

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“…The authors split the survival times into four categories and applied the cross-entropy loss function. A C-index which is commonly used to measure survival model performance, was not reported in that study 21 .…”

Section: Introductionmentioning

confidence: 95%

Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide Images

Jiang

Zanazzi

Hassanpour

2021

Preprint

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We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the grade 3 gliomas TCGA dataset, which was not used for training, our models were able to predict survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.

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Section: Introductionmentioning

confidence: 95%

Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide Images

Jiang

Zanazzi

Hassanpour

2021

Preprint

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“…Survival outcomes are particularly challenging to develop rigorous models for using histology from TCGA, and model performance may be falsely elevated not only by the disparate outcomes across sites, but also the site level differences in critical factors relevant to survival such as stage and age. Studies demonstrating histologic discrimination of survival and recurrence in glioblastoma 41,43,44 , renal cell cancer 41 , and lung cancer 45 patients from TCGA which lack external validation cohorts may have biased estimates of outcome. Prediction of survival may also suffer from this bias 46 even when correcting for age, stage, and sex, as other factors that vary by site also contribute to outcome, ranging from ethnicity of enrollees, to the treatment available at academic vs community centers.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

Howard¹,

Dolezal²,

Kochanny³

et al. 2020

Preprint

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The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. This site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the digital image characteristics constituting this histologic batch effect. As an example, we show that patient ethnicity within the TCGA breast cancer cohort can be inferred from histology due to site-level batch effect, which must be accounted for to ensure equitable application of DL. Batch effect also leads to overoptimistic estimates of model performance, and we propose a quadratic programming method to guide validation that abrogates this bias.

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“…Other cancer types also take advantage of CNNs and exploit GPU computing power. Such is the case with brain cancer, for which condition patient survival can be estimated by means of the recently published classifier DeepSurvNet [ 132 ]. DeepSurvNet builds CNN models implemented with Keras and TensorFlow libraries, which are trained with a dataset from the TCGA Program [ 146 ].…”

Section: Application Of ML Approaches In Cancer Casesmentioning

confidence: 99%

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Banegas-Luna

Peña‐García

Iftene

et al. 2021

IJMS

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Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.

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DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

Cited by 41 publications

References 53 publications

Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide Images

Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide Images

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

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