Machine Learning in oncology: A clinical appraisal

Cuocolo, Renato; Caruso, Martina; Perillo, Teresa; Ugga, Lorenzo; Petretta, Mario

doi:10.1016/j.canlet.2020.03.032

Cited by 125 publications

(108 citation statements)

References 75 publications

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“…Machine learning-based tools have developed rapidly in medical imaging in recent years, especially in oncology. Various machine learning algorithms have been applied to create decision models that aid in clinical diagnosis and treatment ( 8 , 9 ). Few recent studies have used radiomics analysis to identify sacral tumors with a relatively small sample size ( 1 , 5 , 10 ).…”

Section: Introductionmentioning

confidence: 99%

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

et al. 2020

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Purpose: To assess the performance of deep neural network (DNN) and machine learning based radiomics on 3D computed tomography (CT) and clinical characteristics to predict benign or malignant sacral tumors. Materials and methods: This single-center retrospective analysis included 459 patients with pathologically proven sacral tumors. After semi-automatic segmentation, 1,316 hand-crafted radiomics features of each patient were extracted. All models were built on training set (321 patients) and tested on validation set (138 patients). A DNN model and four machine learning classifiers (logistic regression [LR], random forest [RF], support vector machine [SVM] and k-nearest neighbor [KNN]) based on CT features and clinical characteristics were built, respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results: In total, 459 patients (255 males, 204 females; mean age of 42.1 ± 17.8 years, range 4-82 years) were enrolled in this study, including 206 cases of benign tumor and 253 cases of malignant tumor. The sex, age and tumor size had significant differences between the benign tumors and malignant tumors (c 2 sex = 10.854, Z age = −6.616, Z size = 2.843, P < 0.05). The radscore, sex, and age were important indicators for differentiating benign and malignant sacral tumors (odds ratio [OR]1 = 2.492, OR2 = 2.236, OR3 = 1.037, P < 0.01). Among the four clinical-radiomics models (RMs), clinical-LR had the best performance in the validation set (AUC = 0.84, ACC = 0.81). The clinical-DNN model also achieved a high performance (an AUC of 0.83 and an ACC of 0.76 in the validation set) in identifying benign and malignant sacral tumors. Conclusions: Both the clinical-LR and clinical-DNN models would have a high impact on assisting radiologists in their clinical diagnosis of sacral tumors.

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Section: Introductionmentioning

confidence: 99%

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

et al. 2020

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“…The applications of machine learning methods, which learn functional relationships from data, had been largely increased in cancer research and drug discovery (Angermueller et al, 2016;Borisov and Buzdin, 2019;Vamathevan et al, 2019;Cuocolo et al, 2020). One important application of machine learning is medical images, and image-based recognition with machine learning had been increasingly applied to diagnosis in various medical fields (Cuocolo et al, 2020). Esophagogastroduodenoscopy (EGD) is the standard procedure for gastric cancer diagnosis.…”

Section: Machine Learning In Gastric Cancer Researchmentioning

confidence: 99%

“…Not only cancer diagnostics, machine leaning also brings personalized treatment to clinics (Borisov and Buzdin, 2019;Cuocolo et al, 2020). Surgery is the primary treatment for gastric cancer, while the high incidence of distant metastases and the local recurrence of most gastric cancer patients, especially those with advanced gastric cancer, have paved the way for adjuvant therapy (Janunger et al, 2001;Sitarz et al, 2018).…”

Section: Machine Learning In Gastric Cancer Researchmentioning

confidence: 99%

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment

Shi

Wei

2020

Front. Mol. Biosci.

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Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of datadriven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics.

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“…Artificial intelligence (AI) technology, particularly deep learning, has shown remarkable progress in medical image interpretation (22)(23)(24). A typical deep learning approach, named convolutional neural network (CNN), is a novel and powerful tool for the image-based determination of complex relationships and has exhibited sophisticated performance for small feature detection and characterization (25)(26)(27).…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer

et al. 2020

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We aimed to develop a deep convolutional neural network (DCNN) model based on computed tomography (CT) images for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC). A total of 544 patients with AGC were retrospectively enrolled. Seventy-nine patients were confirmed with OPM during surgery or laparoscopy. CT images collected during the initial visit were randomly split into a training cohort and a testing cohort for DCNN model development and performance evaluation, respectively. A conventional clinical model using multivariable logistic regression was also developed to estimate the pretest probability of OPM in patients with gastric cancer. The DCNN model showed an AUC of 0.900 (95% CI: 0.851-0.953), outperforming the conventional clinical model (AUC = 0.670, 95% CI: 0.615-0.739; p < 0.001). The proposed DCNN model demonstrated the diagnostic detection of occult PM, with a sensitivity of 81.0% and specificity of 87.5% using the cutoff value according to the Youden index. Our study shows that the proposed deep learning algorithm, developed with CT images, may be used as an effective tool to preoperatively diagnose OPM in AGC.

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Machine Learning in oncology: A clinical appraisal

Cited by 125 publications

References 75 publications

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment

Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer

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