Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images

Wang, Zhikang; Gao, Qigang; Yi, Xiaoping; Zhang, Xinyu; Zhang, Yiwen; Zhang, Chengqi; Lió, Píetro; Bain, Chris; Bassed, Richard; Li, Shanshan; Guo, Yuming; Imoto, Seiya; Yao, Jianhua; Daly, Roger J.; Song, Jiangning

doi:10.1016/j.cmpb.2023.107733

Cited by 4 publications

(2 citation statements)

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“…For a patient i, we denote by (t i , δ i ) the associated OS time and indicator variable. Based on [12,15], the convention for δ i is :…”

Section: Loss Function and Model Evaluationmentioning

confidence: 99%

“…MIL is also used in this case as an average pooling layer aggregate feature from multiple patches belonging to a specific patient [14]. Furthermore, the recently published Surformer network obtains both global and local WSI features and deploys self and crossattention mechanisms along with a custom loss function to achieve a higher concordance index compared to other methods [15].…”

Section: Introductionmentioning

confidence: 99%

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Overall Survival Time Estimation for Epithelioid Peritoneal Mesothelioma Patients from Whole-Slide Images

Papadopoulos,

Barmpoutis,

Stathaki

et al. 2024

BioMedInformatics

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Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior research has been conducted on image-based survival analysis specifically for peritoneal mesothelioma. Prior studies performed statistical analysis to identify disease factors impacting patients’ survival time. Methods: Therefore, we introduce MPeMSupervisedSurv, a Convolutional Neural Network designed to predict the survival time of patients diagnosed with this disease. We subsequently perform patient stratification based on factors such as their Peritoneal Cancer Index and on whether patients received chemotherapy treatment. Results: MPeMSupervisedSurv demonstrates improvements over comparable methods. Using our proposed model, we performed patient stratification to assess the impact of clinical variables on survival time. Notably, the inclusion of information regarding adjuvant chemotherapy significantly enhances the model’s predictive prowess. Conversely, repeating the process for other factors did not yield significant performance improvements. Conclusions: Overall, MPeMSupervisedSurv is an effective neural network which can predict the survival time of peritoneal mesothelioma patients. Our findings also indicate that treatment by adjuvant chemotherapy could be a factor affecting survival time.

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“…For a patient i, we denote by (t i , δ i ) the associated OS time and indicator variable. Based on [12,15], the convention for δ i is :…”

Section: Loss Function and Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%