Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Chen, Zihang; Lin, Li; Wu, Chengdong; Li, Chaofeng; Xu, Rui‐Hua; Sun, Ying

doi:10.1002/cac2.12215

Cited by 103 publications

(60 citation statements)

References 140 publications

(185 reference statements)

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“…Although AI has achieved promising results in different types of diseases such as diabetes, cancers, and cardiac diseases [22][23][24][25][26], its application in the field of kidney disease has been comparatively limited. e laboratory of the Massachusetts Institute of Technology established an AI prediction system for acute kidney injury.…”

Section: Discussionmentioning

confidence: 99%

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Yan

Lü

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Objective. To establish a prediction model for the risk evaluation of chronic kidney disease (CKD) to guide the management and prevention of CKD. Methods. A total of 1263 patients with CKD and 1948 patients without CKD admitted to the Tongde Hospital of the Zhejiang Province from January 1, 2008, to December 31, 2018, were retrospectively analyzed. Spearman’s correlation was used to analyze the relationship between CKD and laboratory parameters. XGBoost, random forest, Naive Bayes, support vector machine, and multivariate logistic regression algorithms were employed to establish prediction models for the risk evaluation of CKD. The accuracy, precision, recall, F1 score, and area under the receiver operating curve (AUC) of each model were compared. The new bidirectional encoder representations from transformers with light gradient boosting machine (MD-BERT-LGBM) model was used to process the unstructured data and transform it into researchable unstructured vectors, and the AUC was compared before and after processing. Results. Differences in laboratory parameters between CKD and non-CKD patients were observed. The neutrophil ratio and white blood cell count were significantly associated with the occurrence of CKD. The XGBoost model demonstrated the best prediction effect (accuracy = 0.9088, precision = 0.9175, recall = 0.8244, F1 score = 0.8868, AUC = 0.8244), followed by the random forest model (accuracy = 0.9020, precision = 0.9318, recall = 0.7905, F1 score = 0.581, AUC = 0.9519). Comparatively, the predictions of the Naive Bayes and support vector machine models were inferior to those of the logistic regression model. The AUC of all models was improved to some extent after processing using the new MD-BERT-LGBM model. Conclusion. The new MD-BERT-LGBM model with the inclusion of unstructured data has contributed to the higher accuracy, sensitivity, and specificity of the prediction models. Clinical features such as age, gender, urinary white blood cells, urinary red blood cells, thrombin time, serum creatinine, and total cholesterol were associated with CKD incidence.

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Section: Discussionmentioning

confidence: 99%

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Yan

Lü

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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“…Most advancements in AI use one type of data, but deep learning approaches capable of integrating various forms of data will be necessary for holistic precision medicine models. Likewise, medical data is difficult to share between institutions due to confidentiality policies, rendering access to the information necessary for validation and training of new AI technologies difficult ( 113 ).…”

Section: Challenges and Looking Aheadmentioning

confidence: 99%

Functional Drug Screening in the Era of Precision Medicine

2022

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The focus of precision medicine is providing the right treatment to each unique patient. This scientific movement has incited monumental advances in oncology including the approval of effective, targeted agnostic therapies. Yet, precision oncology has focused largely on genomics in the treatment decision making process, and several recent clinical trials demonstrate that genomics is not the only variable to be considered. Drug screening in three dimensional (3D) models, including patient derived organoids, organs on a chip, xenografts, and 3D-bioprinted models provide a functional medicine perspective and necessary complement to genomic testing. In this review, we discuss the practicality of various 3D drug screening models and each model’s ability to capture the patient’s tumor microenvironment. We highlight the potential for enhancing precision medicine that personalized functional drug testing holds in combination with genomic testing and emerging mathematical models.

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“… 13 Recent advances in artificial intelligent image analysis techniques, especially deep learning approaches, have shown promising performance in various histopathological analytic tasks, including diagnosis, prognosis estimation, and gene mutation prediction. 14 , 15 , 16 , 17 There are growing evidences supporting the possible use of deep learning (DL) for H&E stained image-based MMR status detection in CRC, with an area-under-ROC curves (AUROC) between 0·77 and 0·96. 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 Thus, DL is a promising technology that could be further improve the increase detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning

Mei

et al. 2022

eBioMedicine

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Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Cited by 103 publications

References 140 publications

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Functional Drug Screening in the Era of Precision Medicine

Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning

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