Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides

Saillard, Charlie; Schmauch, Benoît; Moarii, Matahi; Toldo, Sylvain; Zaslavskiy, Mikhail; Pronier, Elodie; Laurent, Alexis; Amaddeo, Giuliana; Regnault, Hélène; Sommacale, Danièle; Ziol, Marianne; Pawlotsky, Jean‐Michel; Mulé, Sebastien; Luciani, Alain; Wainrib, Gilles; Clozel, Thomas; Courtiol, Pierre; Caldéraro, Julien

doi:10.1016/s0168-8278(20)31254-x

Cited by 41 publications

(55 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Charlie Saillard et al. ( 31 ) developed two deep learning algorithms for hepatocellular carcinoma, and both models had a higher discriminatory power than a score combining all baseline variables associated with survival, confirming that the deep learning method can help refine the prediction of hepatocellular carcinoma prognosis. Since adenosarcoma is associated with multidimensional factors, the conventional model, the CPH model, for example, could not recognize complex nonlinear relationships between the variables.…”

Section: Discussionmentioning

confidence: 98%

Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study

Liu

Jiao

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

BackgroundThe aim was to develop a personalized survival prediction deep learning model for adenosarcoma patients using the surveillance, epidemiology and end results (SEER) database.MethodsA total of 797 uterine adenosarcoma patients were enrolled in this study. Duplicated and useless variables were excluded, and 15 variables were selected for further analyses, including age, grade, positive lymph nodes or not, marital status, race, tumor extension, stage, and surgery or not. We created our deep survival learning (DSL) model to manipulate the data, which was randomly split into a training set (n = 519, 65%), validation set (n = 143, 18%) and testing set (n = 143, 18%). The Cox proportional hazard (CPH) model was also included comparatively. Finally, personalized survival curves were plotted for randomly selected patients.ResultsThe c-index for the CPH model was 0.726, and the Brier score was 0.17. For our deep survival learning model, we achieved a c-index of 0.774 and a Brier score of 0.14 in the external testing set. In addition, the limitations of the traditional staging system were revealed, and a personalized survival prediction system based on our risk scoring grouping was developed.ConclusionsOur study developed a deep neural network model for adenosarcoma. The performance of this model was superior to that of the traditional Cox proportional hazard model. In addition, a personalized survival prediction system was developed based on our deep survival learning model, which provided more accurate prognostic information for adenosarcoma patients.

show abstract

Section: Discussionmentioning

confidence: 98%

Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study

Liu

Jiao

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Also, one additional advantage of prediction models is that it can retrieve the disease history of a patient and combine them with new clinical information of that patient for a more precise prediction. In fact, the CNN model has been used for the classification and segmentation of liver cancer images, 44 as well as for the prediction and prognosis of diseases 45‐47 …”

Section: Discussionmentioning

confidence: 99%

Liver cancer prediction in a viral hepatitis cohort: A deep learning approach

Phan

Chan

et al. 2020

Intl Journal of Cancer

View full text Add to dashboard Cite

Viral hepatitis is the primary cause of liver diseases, among which liver cancer is the leading cause of death from cancer. However, this cancer is often diagnosed in the later stages, which makes treatment difficult or even impossible. This study applied deep learning (DL) models for the early prediction of liver cancer in a hepatitis cohort. In this study, we surveyed 1 million random samples from the National Health Insurance Research Database (NHIRD) to analyze viral hepatitis patients from 2002 to 2010. Then, we used DL models to predict liver cancer cases based on the history of diseases of the hepatitis cohort. Our results revealed the annual prevalence of hepatitis in Taiwan increased from 2002 to 2010, with an average annual percentage change (AAPC) of 5.8% (95% CI: 4.2-7.4). However, young people (aged 16-30 years) exhibited a decreasing trend, with an AAPC of −5.6 (95% CI: −8.1 to −2.9). The results of applying DL models showed that the convolution neural network (CNN) model yielded the best performance in terms of predicting liver cancer cases, with an accuracy of 0.980 (AUC: 0.886). In conclusion, this study showed an increasing trend in the annual prevalence of hepatitis, but a decreasing trend in young people from 2002 to 2010 in Taiwan. The CNN model may be applied to predict liver cancer in a hepatitis cohort with high accuracy.

show abstract

“…31 In another study, 32 DL has validated its feasibility in predicting overall survival from colorectal cancer histology slides with a hazard ratio of 1.63 in a multivariable Cox proportional hazard model, which can possibly be derived as image-derived prognostic biomarkers. In addition, Saillard et al 33 investigated the DL-based algorithm for patient survival prediction from histology slides after resection of liver cancer. They achieved c-indexes for survival prediction above 0.75 and further validated on The Cancer Genome Atlas dataset, corroborating the capability of AI in prognosis prediction on HCC patients.…”

Section: Artificial Intelligence Image Analysis In Pathologymentioning

confidence: 99%

Potentials of AI in medical image analysis in Gastroenterology and Hepatology

Chen

Sung

2021

J of Gastro and Hepatol

View full text Add to dashboard Cite

With the advancement of artificial intelligence (AI) technology, it comes in a big wave carrying possibly huge impact in the field of medicine. Gastroenterology and hepatology, being a specialty relying much on diagnostic imaging, endoscopy, and histopathology, AI technology has promised improving the quality and consistency of care to the patients. In this review, we will elucidate the development of machine learning methods, especially the visual representation mechanism in deep learning on recognition tasks. Various AI‐image analysis applications in endoscopy, radiology, and pathology are covered in gastroenterology and hepatology and reveal the enormous potentials for AI in assisting diagnosis, prognosis, and treatment. We also discuss the promises as well as pitfalls for AI in medical image analysis and pointing out future research directions.

show abstract

Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides

Cited by 41 publications

References 0 publications

Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study

Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study

Liver cancer prediction in a viral hepatitis cohort: A deep learning approach

Potentials of AI in medical image analysis in Gastroenterology and Hepatology

Contact Info

Product

Resources

About