Artificial intelligence in cancer diagnostics and therapy: current perspectives

Majumder, Anusree; Sen, Debraj

doi:10.4103/ijc.ijc_399_20

Cited by 34 publications

(20 citation statements)

References 52 publications

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“…[14][15][16] Machine learning (algorithm) is a fundamental content of artificial intelligence, in which, data input, output, assignment, and other operations can allow artificial intelligence to calculate and analyze a problem to obtain preliminary results. [16][17][18] This approach can also be used to diagnose and treat diseases. [17] Secondly, artificial intelligence can be used for preclinical drug screening.…”

Section: Discussionmentioning

confidence: 99%

“…[16][17][18] This approach can also be used to diagnose and treat diseases. [17] Secondly, artificial intelligence can be used for preclinical drug screening. [19,20] This is a predictive model for developing drugs for diseases, maximizing drug screening before clinical trials, and providing precision medicine solutions for patients with diseases.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Wang

et al. 2023

Medicine

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To explore the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis. A total of 165 patients with periarthritis of the shoulder, who were admitted to our hospital from January 2020 to January 2022, were selected. Konica SONIMAGE HS1 PLUS color Doppler ultrasound diagnostic instrument was used to detect muscles and bones of patients with scapulohumeral periarthritis. This study proposed an intelligent clustering analysis algorithm with musculoskeletal ultrasound parameters. The neural network was trained on a GeForce RTX 3060 with the Adam W optimizer, a batch size of 12, and an initial learning rate of 5E-4. Each batch of 2 types of trained samples was input into the network in a specific proportion. Pain was assessed on a 10-point visual analogue scale. On the affected side of scapulohumeral periarthritis, the mild pain group showed thickening of the shoulder posterior capsule (2.02 ± 0.72) mm with clear edges. In the moderate pain group, the thickness of the shoulder posterior capsule gradually narrowed (1.01 ± 0.38) mm and became even thinner than that of the unaffected side, with irregular and blurred edges. In the severe pain group, the thickness of the shoulder posterior capsule largely returned to normal (1.21 ± 0.42) mm, and the edge was clear. Multivariate logistic regression showed that in addition to musculoskeletal ultrasound parameters, the length of service, work nature, and the busy degree of work of patients with periarthritis of the shoulder were significant factors that influenced shoulder periarthritis pain ( P < .05). The performance of the proposed intelligent auscultation algorithm was further examined in a clinical setting, in which we used 165 clinical musculoskeletal ultrasound samples (including 81 positive and 84 negative samples) as a test set. The accuracy, sensitivity, and specificity were 0.833, 0.872, and 0.801, respectively. Musculoskeletal ultrasound combined with artificial intelligence algorithm is a new diagnostic and staging tool for scapulohumeral periarthritis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Wang

et al. 2023

Medicine

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“…It enables noninvasive assessment of various tumor characteristics, including cell density and hypoxia, which are indicators of tumor heterogeneity, such as necrosis and increased cell density. Based on these characteristics, we hypothesized that MRI radiological features could be used effectively to predict tumor progression [ 24 , 25 ]. Initially used to distinguish between tumor and normal cervical tissues, MRI has evolved as a promising diagnostic tool for detecting pelvic LNM and monitoring treatment response in patients with cervical cancer [ 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram

Li,

Zhou,

et al. 2023

BMC Med Imaging

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Background Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients. Methods Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox’s proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model’s prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics. Results The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively. Conclusion Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.

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“…Majumder and Sen (2021) [13] centered its focus on the domains of mammary, pulmonary, solid, and encephalic malignancies. The findings embraced the demonstration of artificial intelligence's application in the domains of oncopathology and translational oncology.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Rai,

Yoo

2023

Mathematics

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Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored cancer classification using machine learning and deep learning techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake a comprehensive review of colorectal and gastric cancer detection specifically employing traditional ML classifiers. This review emphasizes the mathematical underpinnings of cancer detection, encompassing preprocessing techniques, feature extraction, machine learning classifiers, and performance assessment metrics. We provide mathematical formulations for these key components. Our analysis is limited to peer-reviewed articles published between 2017 and 2023, exclusively considering medical imaging datasets. Benchmark and publicly available imaging datasets for colorectal and gastric cancers are presented. This review synthesizes findings from 20 articles on colorectal cancer and 16 on gastric cancer, culminating in a total of 36 research articles. A significant focus is placed on mathematical formulations for commonly used preprocessing techniques, features, ML classifiers, and assessment metrics. Crucially, we introduce our optimized methodology for the detection of both colorectal and gastric cancers. Our performance metrics analysis reveals remarkable results: 100% accuracy in both cancer types, but with the lowest sensitivity recorded at 43.1% for gastric cancer.

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Artificial intelligence in cancer diagnostics and therapy: current perspectives

Cited by 34 publications

References 52 publications

Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

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