Artificial intelligence for clinical oncology

Kann, Benjamin H.; Hosny, Ahmed; Aerts, Hugo J.W.L.

doi:10.1016/j.ccell.2021.04.002

Cited by 199 publications

(144 citation statements)

References 90 publications

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“…For better understanding current roles and future perspectives of AI, two important terms/definitions, which are strictly associated with AI, should be enlightened: machine learning and deep learning. Machine learning is a general concept indicating the ability of a machine in learning and thus improving patterns and models of analysis, whereas deep learning indicates a machine-learning method that utilises complex and deep networks to finalise a highly predictive performance [ 3 , 4 ]. Of note, these two concepts are central also in the AI revolution in the management of cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in oncology: current applications and future perspectives

Pea²,

2021

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Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the ‘AI-revolution’ in oncology.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence in oncology: current applications and future perspectives

Pea²,

2021

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“…In this use case, we assigned three modules with different magnifications of 2.5x, 5x, and 20x, which simulates the actual pathological evaluation process and is intuitive for pathologists. Furthermore, if these modules are augmented with those for interpreting radiological images and genetic data instead of WSIs, it will open the door to the realization of explainable multimodal models [45], which will allow for new analytical opportunities such as interdisciplinary relationships between findings.…”

Section: Discussionmentioning

confidence: 99%

MIXTURE of human expertise and deep learning—Developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease

Uegami

Bychkov

Ozasa

et al. 2021

Preprint

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Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer types. Histopathological examination is crucial for its diagnosis and estimation of prognosis. However, the evaluation strongly depends on the experience of pathologists, and the reproducibility of diagnosis is low. Herein, we propose MIXTURE (huMan-In-the-loop eXplainable artificial intelligence Through the Use of REcurrent training), a method to develop deep learning models for extracting pathologically significant findings based on an expert pathologist's perspective with a small annotation effort. The procedure of MIXTURE consists of three steps as follows. First, we created feature extractors for tiles from whole slide images using self-supervised learning. The similar looking tiles were clustered based on the output features and then pathologists integrated the pathologically synonymous clusters. Using the integrated clusters as labeled data, deep learning models to classify the tiles into pathological findings were created by transfer-learning the feature extractors. We developed three models for different magnifications. Using these extracted findings, our model was able to predict the diagnosis of usual interstitial pneumonia, a finding suggestive of progressive disease, with high accuracy (AUC 0.90). This high accuracy could not be achieved without the integration of findings by pathologists. The patients predicted as UIP had significantly poorer prognosis (five-year overall survival [OS]: 55.4% than those predicted as non-UIP (OS: 95.2%). The Cox proportional hazards model for each microscopic finding and prognosis pointed out dense fibrosis, fibroblastic foci, elastosis, and lymphocyte aggregation as independent risk factors. We suggest that MIXTURE may serve as a model approach to different diseases evaluated by medical imaging, including pathology and radiology, and be the prototype for artificial intelligence that can collaborate with humans.

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“…Binary logistic regression analysis is frequently used in analyzing independent risk factors and modeling, which weighs the independent risk factors and generates a linear formula to achieve predictions. Due to the complexity of clinical data distribution, such as multi-dimensional and non-linearly related variables [ 18 ], it is difficult for binary logistic regression analysis to generate a high-performance model. In recent years, the global enthusiasm for machine learning technology based on artificial intelligence seems exponential, and machine learning has achieved impressive results due to improvements in computing power.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy

Liu

Zhao

et al. 2021

JPM

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Anastomotic leakage is a life-threatening complication in patients with gastric adenocarcinoma who received total or proximal gastrectomy, and there is still no model accurately predicting anastomotic leakage. In this study, we aim to develop a high-performance machine learning tool to predict anastomotic leakage in patients with gastric adenocarcinoma received total or proximal gastrectomy. A total of 1660 cases of gastric adenocarcinoma patients who received total or proximal gastrectomy in a large academic hospital from 1 January 2010 to 31 December 2019 were investigated, and these patients were randomly divided into training and testing sets at a ratio of 8:2. Four machine learning models, such as logistic regression, random forest, support vector machine, and XGBoost, were employed, and 24 clinical preoperative and intraoperative variables were included to develop the predictive model. Regarding the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, random forest had a favorable performance with an AUC of 0.89, a sensitivity of 81.8% and specificity of 82.2% in the testing set. Moreover, we built a web app based on random forest model to achieve real-time predictions for guiding surgeons’ intraoperative decision making.

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Artificial intelligence for clinical oncology

Cited by 199 publications

References 90 publications

Artificial intelligence in oncology: current applications and future perspectives

Artificial intelligence in oncology: current applications and future perspectives

MIXTURE of human expertise and deep learning—Developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease

Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy

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