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A review of enhanced biosignature immunotherapy tools for predicting lung cancer immune phenotypes using deep learning.

Cancer has increasingly been recognized as a genetic disease, influenced by lifestyle changes, dietary patterns, and environmental pollutants...

DOI: 10.1007/s12672-025-02771-1

Oliver AS, Sayeed MS, Razak SFA

Discover oncology, 2025-06-01

НОВАЯ СТАТЬЯ

Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.

We used a powerful machine learning framework to highlight the great potential of MacPCD in providing personalised risk assessment and immunotherapy intervention recommendations for KIRC patients.

DOI: 10.1007/s10565-025-10023-9

Liu X, Deng M, Luo X, Li T, Ge Y, Li J, Zhao J, Yang L

Cell biology and toxicology, 2025-06-01

НОВАЯ СТАТЬЯ

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.

Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods...

DOI: 10.1038/s41598-025-04136-8

Thatha VN, Karthik MG, Gaddam VG, Krishna DP, Venkataramana S, Lella KK, Pamula U

Scientific reports, 2025-06-01

НОВАЯ СТАТЬЯ

Intelligent deep learning model for targeted cancer drug delivery.

Nanotechnology and information communication technology (ICT) are being combined to develop innovative drug delivery systems for targeted sites, such as tumor cells...

DOI: 10.1038/s41598-025-96149-6

Kamal IR, El-Atty SMA, El-Zoghdy SF, Soliman RF

Scientific reports, 2025-06-01

НОВАЯ СТАТЬЯ

MLOmics: Cancer Multi-Omics Database for Machine Learning.

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research...

DOI: 10.1038/s41597-025-05235-x

Yang Z, Kotoge R, Piao X, Chen Z, Zhu L, Gao P, Matsubara Y, Sakurai Y, Sun J

Scientific data, 2025-06-01

НОВАЯ СТАТЬЯ

Machine Learning-Driven QSAR Modeling of Anti-Cancer Activity from a Rationally Designed Synthetic Flavone Library.

Flavones, recognized as "privileged scaffolds" in drug discovery, hold significant promise as anti-cancer agents...

DOI: 10.1002/cmdc.202500143

Vijara N, Toopradab B, Yahuafai J, Gulchatchai T, Hairani R, Patigo A, Rungrotmongkol T, Wacharasindhu S, Chavasiri W, Shi L, Maitarad P, Ardkhean R, Khotavivattana T

ChemMedChem, 2025-06-01

НОВАЯ СТАТЬЯ

Construction and Validation of a Predictive Model for Chemotherapy-Induced Peripheral Neuropathy in Breast Cancer Survivors Receiving Taxane-Based Chemotherapy: A Multicenter Cross-sectional Study.

The nomogram model demonstrates good discrimination and calibration, offering a practical and visual tool for identifying high-risk survivors for CIPN.

DOI: 10.1097/NCC.0000000000001516

Li RL, Li J, Liu JE, Bai LX, Liu J, Li C

Cancer nursing, 2025-06-01

НОВАЯ СТАТЬЯ

Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.

In this exploratory study of 45 preoperative CT Neck scans before a neck dissection, ChatGPT-4 V substantially overestimated the degree and severity of lymph node metastasis in head and neck cancer. While these results suggest that ChatGPT-4 V may not yet be a tool providing added value for surgical planning in head and neck cancer, the unparalleled speed of analysis and well-founded reasoning provided suggests that AI tools may provide added value in the future.

DOI: 10.1007/s12672-025-02798-4

Schmidl B, Hoch CC, Walter R, Wirth M, Wollenberg B, Hussain T

Discover oncology, 2025-06-01

НОВАЯ СТАТЬЯ

Advantages of deep learning reconstruction algorithm in ultra-high-resolution CT for the diagnosis of pancreatic cystic neoplasm.

DLR improved image quality and diagnostic performance by effectively reducing image noise and improving lesion conspicuity in the diagnosis of PCNs on UHR-CT. The DLR demonstrated greater diagnostic confidence for the assessment of imaging findings associated with PCNs.

DOI: 10.1007/s11604-025-01804-7

Sofue K, Ueno Y, Yabe S, Ueshima E, Yamaguchi T, Masuda A, Sakai A, Toyama H, Fukumoto T, Hori M, Murakami T

Japanese journal of radiology, 2025-06-01

НОВАЯ СТАТЬЯ

Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.

Study advances current diagnostic efficiency of canine/feline (sub-)cutaneous tumors using machine learning and multimodal imaging data...

DOI: 10.1080/01652176.2025.2510486

Maciulevičius M, Rupšytė G, Raišutis R, Tamošiūnas M

The veterinary quarterly, 2025-06-01

НОВАЯ СТАТЬЯ

Mammogram mastery: Breast cancer image classification using an ensemble of deep learning with explainable artificial intelligence.

Breast cancer is a serious public health problem and is one of the leading causes of cancer-related deaths in women worldwide...

DOI: 10.1097/MD.0000000000042242

Kumar Mondal P, Jahan MK, Byeon H

Medicine, 2025-06-01

НОВАЯ СТАТЬЯ

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

The proposed network outperformed existing approaches, achieving an accuracy of 98.08% on the PANDA dataset and 97.07% on the Gleason-2019 dataset.

DOI: 10.1080/14737140.2025.2512040

Poonkuzhali P, Krishnamoorthy R, Nimma D, Ramesh JVN

Expert review of anticancer therapy, 2025-06-01

Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.

OBJECTIVE: High-dose-rate (HDR) brachytherapy is integral to the standard-of-care for locally advanced cervical cancer (LACC)...

DOI: 10.1088/1361-6560/addea5

Jiang R, Madondo M, Zhang X, Shao Y, Moradi M, Sohn JJ, Wu T, Yang X, Hasan Y, Tian Z

Physics in medicine and biology, 2025-05-31

The Integration of Artificial Intelligence Into Precision Medicine for Neuro-Oncology: Ethical, Clinical, and Nursing Implications in Immunotherapy Care.

This paper explores how artificial intelligence (AI) is being woven into precision medicine for neuro-oncology, highlighting its ethical, clinical, and nursing implications in the realm of immunotherapy...

DOI: 10.7759/cureus.85024

Ajibade VM, Madu CS

Cureus, 2025-05-31

Machine learning identifies SRD5A3 as a propionate-related prognostic biomarker in triple-negative breast cancer.

The increased risk of recurrence and metastasis are obstacles to treating TNBC...

DOI: 10.1038/s41598-025-01274-x

Pan K, Qiu P, Liang H, Wan N, Liu Z, Guo Q

Scientific reports, 2025-05-31

Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.

To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinical staging of cervical cancer...

DOI: 10.1038/s41598-025-03170-w

Zhang M, Zhang Q, Wang X, Peng X, Chen J, Yang H

Scientific reports, 2025-05-31

Recent Advances in Applications of Machine Learning in Cervical Cancer Research: A Focus on Prediction Models.

Artificial intelligence (AI) and machine learning (ML) are transforming cervical cancer research and offering advancements in diagnosis, prognosis, screening, and treatment...

DOI: 10.5468/ogs.25041

Abrar SS, Isa SAM, Hairon SM, Ismail MP, Kadir MNBNA

Obstetrics & gynecology science, 2025-05-31

Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.

Deep learning models using routinely processed WSIs hold promise for DCIS risk stratification, while the benefits of integrating clinical data merit further investigation. Obtaining a larger, high-quality external multicentre dataset would be highly valuable, as successful generalisation of these models could demonstrate their potential to reduce overtreatment in DCIS by enabling active surveillance for women at low risk.

DOI: 10.1016/j.ebiom.2025.105750

Doyle S, Lips EH, Marcus E, Mulder L, Liu YH, Canton FD, Kootstra T, van Seijen MM, Bouybayoune I, Sawyer EJ, Thompson AM, Pinder SE, Sánchez CI, Teuwen J, Wesseling J,

EBioMedicine, 2025-05-31

Leveling Up: Harnessing Cutting-Edge Technology to Enhance Oncology Education and Learning.

The integration and utilization of digital media, gamified learning strategies, and artificial intelligence (AI) are fundamentally transforming the landscape of oncology education and learning...

DOI: 10.1200/EDBK-25-472884

Teplinsky E, Kim TH, Haddad S, Hundal J, Wolbrink T, Chung C

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting, 2025-05-31

Deep learning enables fast and accurate quantification of MRI-guided near-infrared spectral tomography for breast cancer diagnosis.

The utilization of magnetic resonance (MR) im-aging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the specificity and sensitivity of breast cancer diagnosis...

DOI: 10.1109/TMI.2025.3574727

Feng J, Tang Y, Lin S, Jiang S, Xu J, Zhang W, Geng M, Dang Y, Wei C, Li Z, Sun Z, Jia K, Pogue BW, Paulsen KD

IEEE transactions on medical imaging, 2025-05-31

An Explainable Multimodal Artificial Intelligence Model Integrating Histopathological Microenvironment and EHR Phenotypes for Germline Genetic Testing in Breast Cancer.

Genetic testing for pathogenic germline variants is critical for the personalized management of high-risk breast cancers, guiding targeted therapies and cascade testing for at-risk families...

DOI: 10.1002/advs.202502833

Yang Z, Guo C, Li J, Li Y, Zhong L, Pu P, Shang T, Cong L, Zhou Y, Qiao G, Jia Z, Xu H, Cao H, Huang Y, Liu T, Liang J, Wu J, Ma D, Liu Y, Zhou R, Wang X, Ying J, Zhou M, Liu J

Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025-05-31

Response to "Comment on 'Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning'".

У данной статьи нет абстракта.

DOI: 10.1021/acs.analchem.5c02282

Song L, Xue F, Li T, Zhang Q, Xu X, He C, Zhao B, Han XX, Cai L

Analytical chemistry, 2025-05-31

ASO Author Reflections: Artificial Intelligence in Bladder Cancer-Transforming Diagnosis and Treatment Paradigms.

У данной статьи нет абстракта.

DOI: 10.1245/s10434-025-17538-9

Zheng Q

Annals of surgical oncology, 2025-05-31

Machine learning based differential diagnosis of SAPHO syndrome and secondary bone tumors using whole body bone scintigraphy.

SAPHO syndrome is an inflammatory disorder with bone and cutaneous manifestations, for which whole-body bone scintigraphy (WBBS) is frequently used in diagnosis...

DOI: 10.1038/s41598-025-99690-6

Jiang H, Liu A, Cao Y, Lin Z, Jiang H, Liu S, Peng Q, Wu X, Liu Y, Yu X, Wei M, Pan Y, Li C, Ying Z

Scientific reports, 2025-05-31

Interpretable Machine Learning Models for Differentiating Glioblastoma From Solitary Brain Metastasis Using Radiomics.

Machine learning techniques based on radiomics can effectively distinguish GB from SBM, with gradient boosting tree-based models such as LGBMs demonstrating superior performance. High-order features significantly improve model accuracy and robustness. SHAP technology enhances the interpretability and transparency of models for distinguishing brain tumors, providing intuitive visualization of the contribution of radiomic features to classification.

DOI: 10.1016/j.acra.2025.05.016

Xia X, Wu W, Tan Q, Gou Q

Academic radiology, 2025-05-31

Improving Breast Cancer Diagnosis in Ultrasound Images Using Deep Learning with Feature Fusion and Attention Mechanism.

The proposed deep learning model offers superior accuracy in classifying benign and malignant lesions in ultrasound images, outperforming traditional models and radiologists. Its strong performance, coupled with interpretability techniques, demonstrates its potential as a reliable and efficient tool for medical diagnostics.

DOI: 10.1016/j.acra.2025.05.007

Asif S, Yan Y, Feng B, Wang M, Zheng Y, Jiang T, Fu R, Yao J, Lv L, Song M, Sui L, Yin Z, Wang VY, Xu D

Academic radiology, 2025-05-31

Comment on "Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning".

This article discusses current research on the detection of urinary cancers using surface-enhanced Raman spectral analysis of human serum by Song et al., which was published in the Analytical Chemistry journal.

DOI: 10.1021/acs.analchem.5c00517

Bratchenko IA, Bratchenko LA

Analytical chemistry, 2025-05-31

Machine Learning for Predicting Colorectal Cancer and Advanced Colorectal Polyps: A Systematic Review and Meta-Analysis.

The review underscores the significant role of ML in CRC and ACP diagnosis and its routine use could efficiently direct high-risk patients to timely colonoscopies and spare the low-risk ones from unnecessary procedures. Despite the promise shown, the variability in methodologies and outcomes highlights the need for standardized approaches and further investigation in this field.

DOI: 10.1097/MCG.0000000000002172

Malik S, Naqvi A, Tenorio BG, Farrukh F, Tariq R, Adler DG

Journal of clinical gastroenterology, 2025-05-31

Human and Deep Learning Predictions of Peripheral Lung Cancer Using a 1.3 mm Video Endoscopic Probe.

Iriscope could be a valuable tool in PPNs management, especially for experienced operators. Applied to Iriscope images, DL could enhance overall performance of less experienced physicians in diagnosing malignancy.

DOI: 10.1111/resp.70057

Amante E, Ghyselinck R, Thiberville L, Trisolini R, Guisier F, Delchevalerie V, Dumas B, Frénay B, Duparc I, Mazellier N, Farhi C, Jubert C, Salaün M, Lachkar S

Respirology (Carlton, Vic.), 2025-05-31

Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.

In our study, we used machine learning and deep learning to construct surgical approach recommendation models. Deep learning could achieve high performance of prediction and provide efficient and personalized decision support tools for PRTs surgical approach.

DOI: 10.1186/s12880-025-01712-2

Chen Z, Chen Y, Su Y, Jiang N, Wanggou S, Li X

BMC medical imaging, 2025-05-31

Deep learning network enhances imaging quality of low-b-value diffusion-weighted imaging and improves lesion detection in prostate cancer.

DLR1500 DWIs were comparable in quality to original 1500 b-value images within both junior and senior radiology doctors. NAFNet based DWI enhancement can significantly improve the image quality of 800 b-value DWI, and therefore promote the accuracy of prostate cancer lesion detection for junior radiology doctors.

DOI: 10.1186/s12885-025-14354-y

Liu Z, Gu WJ, Wan FN, Chen ZZ, Kong YY, Liu XH, Ye DW, Dai B

BMC cancer, 2025-05-31

Evaluating Surgical Results in Breast Cancer with Artificial Intelligence.

Both ChatGPT and Pyolo8 showed moderate to strong correlation with humans, but ChatGPT demonstrated superior communication skills. However, AI systems may lack the subtlety and empathy required for direct patient interactions, as vulnerable postoperative patients receiving an AI-generated symmetry assessment without appropriate clinical context may experience emotional distress or misinterpret the results. Human oversight and empathetic communication remain essential to ensure quality care while AI is increasingly integrated into medicine.

DOI: 10.1007/s00266-025-04915-8

Kenig N, Monton Echeverria J, Muntaner Vives A

Aesthetic plastic surgery, 2025-05-31

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets.

Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening...

DOI: 10.1038/s41598-025-96864-0

Lopes C, Brandão A, Teixeira MR, Dinis-Ribeiro M, Pereira C

Scientific reports, 2025-05-31

Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.

One possible adverse effect of breast irradiation is the development of pulmonary fibrosis...

DOI: 10.1038/s41598-025-02617-4

Ungvári T, Szabó D, Győrfi A, Dankovics Z, Kiss B, Olajos J, Tőkési K

Scientific reports, 2025-05-31

Large Language Models as a Consulting Hotline for Patients With Breast Cancer and Specialists in China: Cross-Sectional Questionnaire Study.

Currently, compared to other LLMs, ChatGPT-E has demonstrated greater potential for application in educating Chinese patients with breast cancer, and may serve as an effective tool for them to obtain health information. However, for breast cancer specialists, these LLMs are not yet suitable for assisting in clinical diagnosis or treatment activities. Additionally, data security, ethical, and legal risks associated with using LLMs in clinical practice cannot be ignored. In the future, further research is needed to determine the true efficacy of LLMs in clinical scenarios related to breast cancer in China.

DOI: 10.2196/66429

Liu H, Peng J, Li L, Deng A, Huang X, Yin G, Luo H

JMIR medical informatics, 2025-05-31

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation.

This study reveals the multifaceted oncogenic roles of CCT5 and highlights its potential as a pan-cancer biomarker for prognosis and immunotherapy response. The machine learning-derived CCT5.Sig model provides a robust tool for patient stratification and may inform personalized immunotherapy strategies.

DOI: 10.1007/s00262-025-04071-7

Li J, Xu N, Hu L, Xu J, Huang Y, Wang D, Chen F, Wang Y, Jiang J, Hong Y, Ye H

Cancer immunology, immunotherapy : CII, 2025-05-31

Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy.

This is a multi-institutional study to evaluate a head-and-neck CT auto-segmentation software across seven institutions globally...

DOI: 10.1038/s41746-025-01624-z

Pang EPP, Tan HQ, Wang F, Niemelä J, Bolard G, Ramadan S, Kiljunen T, Capala M, Petit S, Seppälä J, Vuolukka K, Kiitam I, Zolotuhhin D, Gershkevitsh E, Lehtiö K, Nikkinen J, Keyriläinen J, Mokka M, Chua MLK

NPJ digital medicine, 2025-05-31

[Influencing factors and predictive model development for contralateral central lymph node metastasis in unilateral papillary thyroid microcarcinoma].

The current study indicates that age, lateral neck lymph node metastasis, ipsilateral lymph node metastasis, and pre-laryngeal lymph node metastasis are significant influencing factors for contralateral CLNM in unilateral PTMC. The predictive model developed using these factors demonstrates good discrimination, calibration, and clinical utility.

DOI: 10.3760/cma.j.cn112137-20241216-02837

Chen ZH, Shui CY, Wang AL, Xu N, Hu BT, Sun RH, Li C, Dong S

Zhonghua yi xue za zhi, 2025-05-31

Computational modeling of breast tissue mechanics and machine learning in cancer diagnostics: enhancing precision in risk prediction and therapeutic strategies.

Integrating FEM and ML represents a paradigm shift in breast cancer management. FEM offers precise modeling of tissue mechanics, while ML excels in predictive analytics and image analysis. Despite challenges such as data variability and limited standardization, synergizing these approaches promises adaptive, personalized care. These computational methods have the potential to redefine diagnostics, optimize treatment, and improve patient outcomes.

DOI: 10.1080/14737140.2025.2508850

Ashi L, Taurin S

Expert review of anticancer therapy, 2025-05-31

Mechanistic study of Lonicerae Japonicae Flos (Caprifoliaceae) in non-small cell lung cancer prevention and treatment through integrative pharmacology, multi-machine learning, artificial intelligence, and in vitro experiments.

LJF has an adjunctive therapeutic effect on NSCLC by regulating PECAM1 and SPP1 targets and their associated signaling pathways.

DOI: 10.1016/j.jep.2025.119832

Liu C, He P, Qiao R, Yang X, Ding C, He F

Journal of ethnopharmacology, 2025-05-31

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ИИ в онкологии

A review of enhanced biosignature immunotherapy tools for predicting lung cancer immune phenotypes using deep learning.

Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.

Intelligent deep learning model for targeted cancer drug delivery.

MLOmics: Cancer Multi-Omics Database for Machine Learning.

Machine Learning-Driven QSAR Modeling of Anti-Cancer Activity from a Rationally Designed Synthetic Flavone Library.

Construction and Validation of a Predictive Model for Chemotherapy-Induced Peripheral Neuropathy in Breast Cancer Survivors Receiving Taxane-Based Chemotherapy: A Multicenter Cross-sectional Study.

Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.

Advantages of deep learning reconstruction algorithm in ultra-high-resolution CT for the diagnosis of pancreatic cystic neoplasm.

Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.

Mammogram mastery: Breast cancer image classification using an ensemble of deep learning with explainable artificial intelligence.

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.

The Integration of Artificial Intelligence Into Precision Medicine for Neuro-Oncology: Ethical, Clinical, and Nursing Implications in Immunotherapy Care.

Machine learning identifies SRD5A3 as a propionate-related prognostic biomarker in triple-negative breast cancer.

Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.

Recent Advances in Applications of Machine Learning in Cervical Cancer Research: A Focus on Prediction Models.

Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.

Leveling Up: Harnessing Cutting-Edge Technology to Enhance Oncology Education and Learning.

Deep learning enables fast and accurate quantification of MRI-guided near-infrared spectral tomography for breast cancer diagnosis.

An Explainable Multimodal Artificial Intelligence Model Integrating Histopathological Microenvironment and EHR Phenotypes for Germline Genetic Testing in Breast Cancer.

Response to "Comment on 'Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning'".

ASO Author Reflections: Artificial Intelligence in Bladder Cancer-Transforming Diagnosis and Treatment Paradigms.

Machine learning based differential diagnosis of SAPHO syndrome and secondary bone tumors using whole body bone scintigraphy.

Interpretable Machine Learning Models for Differentiating Glioblastoma From Solitary Brain Metastasis Using Radiomics.

Improving Breast Cancer Diagnosis in Ultrasound Images Using Deep Learning with Feature Fusion and Attention Mechanism.

Comment on "Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning".

Machine Learning for Predicting Colorectal Cancer and Advanced Colorectal Polyps: A Systematic Review and Meta-Analysis.

Human and Deep Learning Predictions of Peripheral Lung Cancer Using a 1.3 mm Video Endoscopic Probe.

Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.

Deep learning network enhances imaging quality of low-b-value diffusion-weighted imaging and improves lesion detection in prostate cancer.

Evaluating Surgical Results in Breast Cancer with Artificial Intelligence.

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets.

Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.

Large Language Models as a Consulting Hotline for Patients With Breast Cancer and Specialists in China: Cross-Sectional Questionnaire Study.

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation.

Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy.

[Influencing factors and predictive model development for contralateral central lymph node metastasis in unilateral papillary thyroid microcarcinoma].

Computational modeling of breast tissue mechanics and machine learning in cancer diagnostics: enhancing precision in risk prediction and therapeutic strategies.

Mechanistic study of Lonicerae Japonicae Flos (Caprifoliaceae) in non-small cell lung cancer prevention and treatment through integrative pharmacology, multi-machine learning, artificial intelligence, and in vitro experiments.

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