Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Huang, Shigao; Yang, Jie; Fong, Simon; Zhao, Qi

doi:10.1016/j.canlet.2019.12.007

Cited by 389 publications

(262 citation statements)

References 102 publications

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“…The purpose of image examination today is not only qualitative diagnosis but also obtaining rich quantitative information such as the severity of the disease, prognosis, therapeutic effect of drugs, etc. (29,30), in which arti cial intelligence will make an important difference.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Locally Advanced Nasopharyngeal Carcinoma Prognostication Based on Pre- and Post-treatment MRI

Deng

Hua

et al. 2020

Preprint

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Background: Although it has been reported by several studies that using AI to predict the prognosis of nasopharyngeal carcinoma (NPC) based on magnetic resonance (MR) image, the information around the tumor was not valued and the post-treatment MR images were ignored. Herein we aimed to predict the prognosis of advanced NPC (stage Ⅲ-Ⅳa) using pre- and post-treatment MR images based on deep learning (DL).Methods: A total of 206 patients with primary NPC who were diagnosed and treated at the Renmin Hospital of Wuhan University between June 2012 and January 2018 were retrospectively selected. A rectangular region of interest (ROI), which included the tumor area, surrounding tissues and organs, was delineated on each pre- and post-treatment MR image. Two InceptionResnetV2-based transfer learning models, named pre-model and post-model, were trained with the Pre-dataset and the Post-dataset, respectively. In addition, an ensemble learning model based on the pre-model and post-models was trained. The three established models were evaluated by receiver operating characteristic (ROC) analysis, confusion matrix, and Harrell’s concordance indices (C-index) after the model test. High-risk-related heat maps were developed according to the DL models.Results: The pre-model, post-model, and ensemble models displayed a C-index of 0.717 (95% CI: 0.639 to 0.795), 0.811 (95% CI: 0.745–0.877), 0.830 (95% CI: 0.767–0.893), and AUC of 0.745 (95% CI: 0.592–0.897), 0.820 (95% CI: 0.687–0.953), and 0.841 (95% CI: 0.715–0.968) for the test cohort, respectively. In comparison with the models, the post-model performance was better than the pre-model, which indicated the importance of post-treatment images for prognosis prediction. All three DL models performed better than the TNM staging system. The captured features presented on heat maps showed that the areas around the tumor and lymph nodes were related to the prognosis of the tumor.Conclusions: The three established DL models based on pre- and post-treatment MR images have a better performance than TNM staging. Post-treatment MR images are of great significance for prognosis prediction and could contribute to clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Locally Advanced Nasopharyngeal Carcinoma Prognostication Based on Pre- and Post-treatment MRI

Deng

Hua

et al. 2020

Preprint

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“…Because AI is considered as the foremost advanced approach in cancer research, 5–10 we then used AI methods for exploring the DNp73 expression with respect to 5‐year survival prediction and prognosis. The methods consist of 10 pretrained convolutional neural networks (CNNs) whose properties are listed in Table S3 in the Supporting Information.…”

Section: Figurementioning

confidence: 99%

Artificial intelligence–based 5‐year survival prediction and prognosis of DNp73 expression in rectal cancer patients

Pham

Fan

Zhang

et al. 2020

Clinical & Translational Med

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Dear Editor, Preoperative radiotherapy (pRT) is known to improve local control for rectal cancer patients besides surgery. 1-3 However, there are many patients who do not respond to pRT but experience side effects. It is therefore urgently required to find promising pRT-related biomarkers for approaching precision medicine. In this study, we investigated the application of artificial intelligence (AI) for discovering the predictive and prognostic power of the DNp73 expression in a cohort of 143 rectal cancer patients from the Swedish rectal cancer trial of pRT. 2 The DNp73 expression was identified by immunohistochemistry (IHC), and the procedure for the IHC image extraction was described in Ref. 4. While the manual pathology-based analysis of DNp73 expression did not provide any survival information (> .05), the average AI-based validation results show very high accuracy rates (≥93%) for the 5-year prediction and prognosis of the rectal cancer patients either with or without pRT. The DNp73 expression was investigated in 96 biopsies, surgically resected normal and tumor samples from 77 patients without pRT and 59 patients with pRT (Figure 1A,B). The DNp73 staining was performed in the whole group of surgically resected distant normal (= 119), adjacent normal (= 79), and tumor samples (= 136). Strong cytoplasmic DNp73 staining was present in the normal and tumor cells (Figure 1A,B). In the analysis of the clinicopathologic and biologic significance of DNp73 expression, we divided the patients into DNp73 weak and strong groups. The expression of DNp73 was significantly increased in the tumors either without or with pRT, when compared with the normal mucosa (Figure 1C, < .001). The significant differences of the DNp73 expression were observed in the matched cases of the distant normal mucosa, adjacent normal mucosa, and tumor derived from the same patient (Figure 1D, = .002). We found that the DNp73 expression in the biopsies was not related to any clinicopathologic variables including gender, age, differentiation, surgical type, local recurrence, distant recurrence, This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…La inteligencia artificial (IA), especialmente el aprendizaje automático y el aprendizaje profundo, ha encontrado aplicaciones populares en la investigación del cáncer de mama en los últimos años, el rendimiento de la predicción del cáncer ha ido aumentando. Huang, Yang, Fong y Zhao han revisado la literatura sobre la aplicación de la IA al diagnóstico y pronóstico del cáncer, y resume sus ventajas, proporcionando una nueva perspectiva sobre cómo la tecnología de IA puede ayudar a mejorar el diagnóstico y el pronóstico del cáncer, y continuar mejorando la salud humana en el futuro [10].…”

Section: Trabajos Relacionadosunclassified

Predicción de cáncer de mama a través de biomarcadores mediante aprendizaje automático

Quintanilla¹,

Medina²,

Sulla-Torres³

2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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The prediction of breast cancer through biomarkers is proposed through machine learning, in order to minimize the waiting time that exists at the time a cancer is discarded due to the different factors that exist in our national reality. For this, a Neural Network has been used, which is an Automatic Learning algorithm that allowed us to make the prediction. The results showed that with the developed design of the Neural Network an accuracy of 82.76% was obtained, likewise, a prototype was built that allowed validating the proposal, with which it can be concluded that the Neural Network is an adequate algorithm to be used for Complementary to the prediction of breast cancer through biomarkers and that the developed prototype serves those interested in the oncology field.

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Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Cited by 389 publications

References 102 publications

Deep Learning for Locally Advanced Nasopharyngeal Carcinoma Prognostication Based on Pre- and Post-treatment MRI

Deep Learning for Locally Advanced Nasopharyngeal Carcinoma Prognostication Based on Pre- and Post-treatment MRI

Artificial intelligence–based 5‐year survival prediction and prognosis of DNp73 expression in rectal cancer patients

Predicción de cáncer de mama a través de biomarcadores mediante aprendizaje automático

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