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Machine Learning-Based Prognostic Gene Signature for Early Triple Negative Breast Cancer.

This study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology.

DOI: 10.4143/crt.2024.937

Kim JW, Lee J, Lee SH, Ahn S, Park KH

Cancer research and treatment, 2024-11-21

НОВАЯ СТАТЬЯ

XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.

Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women's health...

DOI: 10.1038/s41598-024-79620-8

Srinivasu PN, Jaya Lakshmi G, Gudipalli A, Narahari SC, Shafi J, Woźniak M, Ijaz MF

Scientific reports, 2024-11-21

НОВАЯ СТАТЬЯ

Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer.

Early Diagnosis of oral cancer is very important and can save you from some oral malignancies...

DOI: 10.1038/s41598-024-79250-0

Wei X, Chanjuan L, Ke J, Linyun Y, Jinxing G, Quanbing W

Scientific reports, 2024-11-21

НОВАЯ СТАТЬЯ

Interpretable machine learning model for digital lung cancer prescreening in Chinese populations with missing data.

We developed an interpretable model, BOUND (Bayesian netwOrk for large-scale lUng caNcer Digital prescreening), using a comprehensive EHR dataset from the China to improve lung cancer detection rates...

DOI: 10.1038/s41746-024-01309-z

Zhang S, Wang Q, Hu X, Zhang B, Sun S, Yuan Y, Jia X, Yu Y, Xue F

NPJ digital medicine, 2024-11-21

НОВАЯ СТАТЬЯ

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.

This study of 2886 veterans showed evidence of heterogeneity and that survival days may be improved for certain patients with mCRPC based on the medication prescribed. Findings suggest that prescription rules based on the patient characteristics, laboratory test results, and comorbidities available to the physician at the time of prescription could improve survival by providing personalized treatment decisions.

DOI: 10.2196/59480

Gopukumar D, Menon N, Schoen MW

JMIR medical informatics, 2024-11-21

НОВАЯ СТАТЬЯ

Application of artificial intelligence combined with MDT teaching model in colorectal cancer training teaching.

У данной статьи нет абстракта.

DOI: 10.23736/S2724-5985.24.03784-7

Liu J, Zhang N, Li B, Cai S

Minerva gastroenterology, 2024-11-21

НОВАЯ СТАТЬЯ

Artificial Intelligence can Facilitate Application of Risk Stratification Algorithms to Bladder Cancer Patient Case Scenarios.

GPT-4 can accurately risk stratify patients with respect to NMIBC when provided with context containing guidelines. Overestimation of risk is more common than underestimation, and intermediate risk NMIBC is most likely to be incorrectly stratified. With further validation, GPT-4 can become a tool for risk stratification of NMIBC in clinical practice.

DOI: 10.1177/11795549241296781

Yudovich MS, Alzubaidi AN, Raman JD

Clinical Medicine Insights. Oncology, 2024-11-21

НОВАЯ СТАТЬЯ

Enhancing clinical decision-making in endometrial cancer through deep learning technology: A review of current research.

Endometrial cancer (EC), a growing malignancy among women, underscores an urgent need for early detection and intervention, critical for enhancing patient outcomes and survival rates...

DOI: 10.1177/20552076241297053

Jiang X, Feng C, Sun W, Feng L, Hao Y, Liu Q, Cui B

Digital health, 2024-11-21

НОВАЯ СТАТЬЯ

Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity.

The use of AI in the analysis of cytopathological samples in research and clinical settings is increasing, and the involvement of pathologists in AI workflows is becoming increasingly important.

DOI: 10.1186/s40001-024-02138-2

Hays P

European journal of medical research, 2024-11-21

НОВАЯ СТАТЬЯ

Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer.

The proposed AI-based algorithm accurately predicted the survival of each patient with advanced NSCLC. The AI-based methodology will contribute to personalized medicine.

DOI: 10.1186/s12885-024-13190-w

Koyama J, Morise M, Furukawa T, Oyama S, Matsuzawa R, Tanaka I, Wakahara K, Yokota H, Kimura T, Shiratori Y, Kondoh Y, Hashimoto N, Ishii M

BMC cancer, 2024-11-21

НОВАЯ СТАТЬЯ

A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach.

The neural network model exhibited ideal predictive performance in the survival status of NSCLC patients, and the decision tree model constructed based on selected important variables was conducive to rapid bedside prognosis assessment and decision-making.

DOI: 10.1186/s12911-024-02753-3

Wang Y, Mei N, Zhou Z, Fang Y, Lin J, Zhao F, Fang Z, Li Y

BMC medical informatics and decision making, 2024-11-21

НОВАЯ СТАТЬЯ

Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.

The clinical-deep learning radiomic model can effectively predict EF in patients with advanced esophageal cancer undergoing radiotherapy or chemoradiotherapy.

DOI: 10.1186/s12880-024-01473-4

Zhang Y, Cheng X, Luo X, Sun R, Huang X, Liu L, Zhu M, Li X

BMC medical imaging, 2024-11-21

НОВАЯ СТАТЬЯ

Prior information guided deep-learning model for tumor bed segmentation in breast cancer radiotherapy.

The prior tumor contours on both pre-operative and post-operative CTs provide valuable information in searching for the precise location of TB on post-operative CT. The proposed method provided a feasible way to assist auto-segmentation of TB in treatment planning of radiotherapy after breast-conserving surgery.

DOI: 10.1186/s12880-024-01469-0

Huang P, Yan H, Shang J, Xie X

BMC medical imaging, 2024-11-21

НОВАЯ СТАТЬЯ

Current status and future direction of cancer research using artificial intelligence for clinical application.

The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning...

DOI: 10.1111/cas.16395

Hamamoto R, Komatsu M, Yamada M, Kobayashi K, Takahashi M, Miyake M, Jinnai S, Koyama T, Kouno N, Machino H, Takahashi S, Asada K, Ueda N, Kaneko S

Cancer science, 2024-11-21

НОВАЯ СТАТЬЯ

Correspondence on 'Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study' by Ye .

У данной статьи нет абстракта.

DOI: 10.1136/jitc-2024-010828

Ren Q

Journal for immunotherapy of cancer, 2024-11-21

НОВАЯ СТАТЬЯ

Machine learning identifies immune-based biomarkers that predict efficacy of anti-angiogenesis-based therapies in advanced lung cancer.

Integrating pre-treatment circulating inflammatory biomarkers could non-invasively and early forecast clinical outcomes for anti-angiogenic response in lung cancer. The immune-based prognostic model is a promising tool to reflect systemic inflammatory status and predict clinical prognosis for anti-angiogenic treatment in patients with stage III-IV lung cancer.

DOI: 10.1016/j.intimp.2024.113588

Chen P, Cheng L, Zhao C, Tang Z, Wang H, Shi J, Li X, Zhou C

International immunopharmacology, 2024-11-21

НОВАЯ СТАТЬЯ

Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

This study demonstrated that the DenseNet121 model has the greatest potential in distinguishing CLNM from non-CLNM in patients with PTC. Dynamic videos also offered more information about the disease states which have proven to be more efficient and robust in identifying CLNM compared to statis images.

DOI: 10.1007/s12020-024-04091-w

Qian T, Zhou Y, Yao J, Ni C, Asif S, Chen C, Lv L, Ou D, Xu D

Endocrine, 2024-11-21

НОВАЯ СТАТЬЯ

Everolimus treatment in patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative advanced breast cancer and a predictive model for its efficacy: a multicenter real-world study.

A predictive model was successfully established to predict survival outcomes for everolimus in patients with HR+/HER2- ABC, which may provide references for the management of everolimus in Chinese patients with HR+/HER2- ABC.

DOI: 10.1177/17588359241292256

Tan Y, Peng Z, Jiang H, Ma F, Wang J, Zhang P, Li Q, Tian X, Han Y, Ji D, Xu B, Zhao W, Fan Y

Therapeutic advances in medical oncology, 2024-11-21

НОВАЯ СТАТЬЯ

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning.

These promising results highlight its potential as a key tool in the future development of diagnostic and therapeutic strategies for liver cancers. Lasers Surg. Med. 00:00-00, 2024. 2024 Wiley Periodicals LLC.

DOI: 10.1002/lsm.23861

Potapova EV, Shupletsov VV, Dremin VV, Zherebtsov EA, Mamoshin AV, Dunaev AV

Lasers in surgery and medicine, 2024-11-21

НОВАЯ СТАТЬЯ

Artificial intelligence real-time mapping with Eureka during robotic total mesorectal excision for rectal cancer: A video vignette.

У данной статьи нет абстракта.

DOI: 10.1111/codi.17240

Piozzi GN, Duhoky R, Przedlacka A, Ronconi Di Giuseppe D, Khan JS

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland, 2024-11-21

НОВАЯ СТАТЬЯ

Deep learning for oncologic treatment outcomes and endpoints evaluation from CT scans in liver cancer.

Accurate treatment response assessment using serial CT scans is essential in oncological clinical trials...

DOI: 10.1038/s41698-024-00754-z

Xia Y, Zhou J, Xun X, Johnston L, Wei T, Gao R, Zhang Y, Reddy B, Liu C, Kim G, Zhang J, Zhao S, Yu Z

NPJ precision oncology, 2024-11-21

НОВАЯ СТАТЬЯ

Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study.

Our study offers a comprehensive comparison between ML models and the MSKCC nomogram for predicting NSLN metastasis among Iranian breast cancer patients. These findings contribute valuable insights to the discourse on personalized treatment approaches, emphasizing the need for tailored prognostic tools across diverse populations. The implications of this research extend to clinical decision-making, potentially improving the accuracy and efficacy of breast cancer management within the Iranian healthcare landscape.

DOI: 10.1016/j.compbiomed.2024.109412

Fattahi AS, Hoseini M, Dehghani T, Nezhad Noor Nia RG, Naseri Z, Ebrahimzadeh A, Mahri A, Eslami S

Computers in biology and medicine, 2024-11-21

НОВАЯ СТАТЬЯ

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

Cancer is one of the most serious health problems worldwide...

DOI: 10.1039/d4an00784k

Ma L, Gao W, Hu X, Zhou D, Wang C, Yu J, Tang K

The Analyst, 2024-11-21

НОВАЯ СТАТЬЯ

Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice.

This study could not demonstrate an improvement in the esophageal cancer detection rate using the AI diagnostic support system for ESCC.

DOI: 10.1055/a-2421-3194

Nakao E, Yoshio T, Kato Y, Namikawa K, Tokai Y, Yoshimizu S, Horiuchi Y, Ishiyama A, Hirasawa T, Kurihara N, Ishizuka N, Ishihara R, Tada T, Fujisaki J

Endoscopy, 2024-11-21

Correction: Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.

У данной статьи нет абстракта.

DOI: 10.1007/s12094-024-03780-2

Muñoz Martín AJ, Lecumberri R, Souto JC, Obispo B, Sanchez A, Aparicio J, Aguayo C, Gutierrez D, García Palomo A, Benavent D, Taberna M, Viñuela-Benéitez MC, Arumi D, Hernández-Presa MÁ

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2024-11-21

Correction: Multiparametric MRI based deep learning model for prediction of early recurrence of hepatocellular carcinoma after SR following TACE.

У данной статьи нет абстракта.

DOI: 10.1007/s00432-024-06027-3

Wang H, Li J, Ouyang Y, Ren H, An C, Liu W

Journal of cancer research and clinical oncology, 2024-11-18

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ИИ в онкологии

Machine Learning-Based Prognostic Gene Signature for Early Triple Negative Breast Cancer.

XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.

Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer.

Interpretable machine learning model for digital lung cancer prescreening in Chinese populations with missing data.

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.

Application of artificial intelligence combined with MDT teaching model in colorectal cancer training teaching.

Artificial Intelligence can Facilitate Application of Risk Stratification Algorithms to Bladder Cancer Patient Case Scenarios.

Enhancing clinical decision-making in endometrial cancer through deep learning technology: A review of current research.

Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity.

Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer.

A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach.

Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.

Prior information guided deep-learning model for tumor bed segmentation in breast cancer radiotherapy.

Current status and future direction of cancer research using artificial intelligence for clinical application.

Correspondence on 'Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study' by Ye .

Machine learning identifies immune-based biomarkers that predict efficacy of anti-angiogenesis-based therapies in advanced lung cancer.

Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

Everolimus treatment in patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative advanced breast cancer and a predictive model for its efficacy: a multicenter real-world study.

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning.

Artificial intelligence real-time mapping with Eureka during robotic total mesorectal excision for rectal cancer: A video vignette.

Deep learning for oncologic treatment outcomes and endpoints evaluation from CT scans in liver cancer.

Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study.

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice.

Correction: Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.

Correction: Multiparametric MRI based deep learning model for prediction of early recurrence of hepatocellular carcinoma after SR following TACE.

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