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ИИ в онкологии

Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case-control study.

Our study has offered a highly effective serum SERS-based approach for multi-cancer early detection, which might shed new light on cancer screening in clinical practice.

DOI: 10.1186/s12916-025-03887-5

Lin Y, Zhang Q, Chen H, Liu S, Peng K, Wang X, Zhang L, Huang J, Yan X, Lin X, Hasan UMD, Sarwara M, Fu F, Feng S, Wang C

BMC medicine, 2025-02-23

НОВАЯ СТАТЬЯ

Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.

Interest in ML-based clinical decision-making has grown in recent years alongside increased multi-institutional collaboration. However, small sample sizes likely impacted data quality and generalizability. Persistent challenges include limited international validation across ethnicities, inconsistent data sharing, disparities in validation metrics, and insufficient calibration reporting, hindering model comparison reliability.

DOI: 10.1186/s12874-025-02463-y

Santos CS, Amorim-Lopes M

BMC medical research methodology, 2025-02-23

НОВАЯ СТАТЬЯ

Validation of a digital pathology-based multimodal artificial intelligence biomarker in a prospective, real-world prostate cancer cohort treated with prostatectomy.

The MMAI biomarker, previously shown to be prognostic for distant metastasis and prostate cancer-specific mortality in men receiving definitive radiation, was prognostic for post-RP endpoints: BCR and AP. This biomarker validation study further supports the use of MMAI biomarkers in men with PCa outside North America and those treated with RP.

DOI: 10.1158/1078-0432.CCR-24-3656

Bjartell A, Krzyzanowska A, Liu VYT, Tierney M, Royce TJ, Sjöström M, Palominos-Rivera MM, Chen E, Kraft A, Esteva A, Feng FY

Clinical cancer research : an official journal of the American Association for Cancer Research, 2025-02-23

НОВАЯ СТАТЬЯ

Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.

The viability of cells and the integrity of the genome depend on the detection and repair of damaged DNA through intricate mechanisms...

DOI: 10.1007/s11030-025-11133-6

Iqbal AB, Masoodi TA, Bhat AA, Macha MA, Assad A, Shah SZA

Molecular diversity, 2025-02-23

Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.

Breast cancer (BC) remains a significant threat to women's health worldwide...

DOI: 10.1002/cai2.159

Feng K, Yi Z, Xu B

Cancer innovation, 2025-02-22

A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy.

Comparable results to a 3D U-Net and clinical plans can be reached with a 2D HD U-net for a dataset size commonly seen in clinical practice. The Attention U-Net did profit from adding extra channels and data augmentation but did not reach the same level of accuracy as the other models.

DOI: 10.1002/acm2.70047

Klarenberg R, Bakx NLM, Hurkmans CW

Journal of applied clinical medical physics, 2025-02-22

Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer.

This study showed the efficacy of machine learning models in predicting the basal and luminal subtypes of BLCA based on the extraction of deep learning features from tumor patches in H&E-stained WSIs. The performance of the LR model suggests that the integration of AI tools into the diagnostic process could significantly enhance the accuracy of molecular subtyping, thereby potentially informing personalized treatment strategies for BLCA patients.

DOI: 10.1186/s12885-025-13688-x

Zheng Z, Dai F, Liu J, Zhang Y, Wang Z, Wang B, Qiu X

BMC cancer, 2025-02-22

Development and internal validation of a predictive model of overall and progression-free survival in eribulin-treated patients with breast cancer based on baseline peripheral blood parameters.

Nomograms incorporating peripheral blood parameters may improve the accuracy of predicting OS and PFS in patients treated with eribulin. Our prediction model may help decision-making for breast cancer patients who are considering eribulin treatment.

DOI: 10.1007/s12282-025-01678-7

Natori K, Igeta M, Morimoto T, Nagahashi M, Akashi-Tanaka S, Daimon T, Miyoshi Y

Breast cancer (Tokyo, Japan), 2025-02-22

Machine Learning-Driven Modeling to Predict Postdischarge Venous Thromboembolism After Pancreatectomy for Pancreas Cancer.

Machine learning models can reliably identify patients who are at high risk for pdVTE. Such models should be used to inform prescription of extended VTE prophylaxis.

DOI: 10.1245/s10434-025-17032-2

Ahmed KS, Issaka SM, Marcinak CT, Virani SS, Jaraczewski T, Afshar M, Mayampurath A, Churpek MM, Mathew J, Zafar SN

Annals of surgical oncology, 2025-02-22

A predictive model for neoadjuvant therapy response in breast cancer.

Our study's findings highlight the crucial role of metabolomics in advancing personalized medicine for breast cancer treatment by effectively identifying metabolite biomarkers correlated with neoadjuvant therapy response. This approach signifies a critical step towards tailoring treatment plans based on individual metabolic profiles, ultimately improving patient outcomes in breast cancer care.

DOI: 10.1007/s11306-025-02230-6

Nambo-Venegas R, Enríquez-Cárcamo VI, Vela-Amieva M, Ibarra-González I, Lopez-Castro L, Cabrera-Nieto SA, Bargalló-Rocha JE, Villarreal-Garza CM, Mohar A, Palacios-González B, Reyes-Grajeda JP, Fajardo-Espinoza FS, Cruz-Ramos M

Metabolomics : Official journal of the Metabolomic Society, 2025-02-22

Predicting hepatocellular carcinoma survival with artificial intelligence.

Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory...

DOI: 10.1038/s41598-025-90884-6

Seven İ, Bayram D, Arslan H, Köş FT, Gümüşlü K, Aktürk Esen S, Şahin M, Şendur MAN, Uncu D

Scientific reports, 2025-02-22

Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.

We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wide DNA methylation profiles that is widely employed in the clinic...

DOI: 10.1038/s41467-025-57078-0

Benfatto S, Sill M, Jones DTW, Pfister SM, Sahm F, von Deimling A, Capper D, Hovestadt V

Nature communications, 2025-02-22

The Role of Artificial Intelligence in Gynecologic Oncology Decision Making: A Feasibility Study.

The study demonstrates that AI models, such as Chat-GPT, can to some extent evaluate clinical cases, recognize clinical and/or laboratory abnormalities and make therapy-related suggestions. Despite high overall agreement, differences were predominantly noted in the more complex cases, rendering human interpretation necessary. The findings underscore the benefits of AI in terms of clarity, time efficiency, and cost-effectiveness. Future research should further explore the application of AI to real patient data and development of hybrid decision models to optimize integration into clinical practice.

DOI: 10.1159/000544751

Psilopatis I, Sipulina N, Stuebs FA, Heindl F, Poeschke P, Bader S, Krueckel A, Fasching PA, Beckmann MW, Emons J

Gynecologic and obstetric investigation, 2025-02-22

Validation of patient-specific deep learning markerless lung tumor tracking aided by 4DCBCT.

OBJECTIVE: Tracking tumors with multi-leaf collimators and X-ray imaging can be a cost-effective motion management method to reduce internal target volume margins for lung cancer patients, sparing normal tissues while ensuring target coverage...

DOI: 10.1088/1361-6560/adb89c

Huang L, Thummerer A, Papadopoulou CI, Corradini S, Belka C, Riboldi M, Kurz C, Landry G

Physics in medicine and biology, 2025-02-22

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

The success of TRUSWorthy demonstrates the potential of integrated deep learning solutions to meet clinical needs in a highly challenging deployment setting, and is a significant step toward creating a trustworthy system for computer-assisted PCa diagnosis.

DOI: 10.1007/s11548-025-03335-y

Harmanani M, Wilson PFR, To MNN, Gilany M, Jamzad A, Fooladgar F, Wodlinger B, Abolmaesumi P, Mousavi P

International journal of computer assisted radiology and surgery, 2025-02-22

Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

Through artificial intelligence (AI) algorithms, the PET/CT metabolic and radiomics features combined with clinical features could improve the accuracy of staging prediction.

DOI: 10.1007/s00432-025-06134-9

Xu S, Zhu C, Wu M, Gu S, Wu Y, Cheng S, Wang C, Zhang Y, Zhang W, Shen W, Yang J, Yang X, Wang Y

Journal of cancer research and clinical oncology, 2025-02-22

T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

Both conventional reconstruction and DLR provide acceptable image quality for T2W 3D FSE imaging of rectal cancer. DLR was clinically preferred over conventional reconstruction in almost all categories.

DOI: 10.1002/acm2.70031

Nguyen D, Palmquist S, Hwang KP, Ma J, Salem U, Sun J, Wang X, Son JB, Ernst R, Wei P, Kaur H, Stanietzky N

Journal of applied clinical medical physics, 2025-02-22

The puzzling Spitz tumours: is artificial intelligence the key to their understanding?

Since their first description in 1948, Spitz tumours remain one of the most challenging diagnostic entities in dermatopathology due to their complex histological features and ambiguous clinical behaviour...

DOI: 10.1111/his.15428

Launet L, Colomer A, Mosquera-Zamudio A, Monteagudo C, Naranjo V

Histopathology, 2025-02-22

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.

M-YOLO(Mamba YOLOv10) enhances the identification performance of PCN by integrating the deep feature extraction capability of Mamba and the fast localization technique of YOLOv10.

DOI: 10.1177/08953996251313719

Dai J, He C, Jin L, Chen C, Wu J, Bian Y

Journal of X-ray science and technology, 2025-02-21

Ensemble fuzzy deep learning for brain tumor detection.

This research presents a novel ensemble fuzzy deep learning approach for brain Magnetic Resonance Imaging (MRI) analysis, aiming to improve the segmentation of brain tissues and abnormalities...

DOI: 10.1038/s41598-025-90572-5

Belhadi A, Djenouri Y, Belbachir AN

Scientific reports, 2025-02-21

NMTNet: A Multi-task Deep Learning Network for Joint Segmentation and Classification of Breast Tumors.

Segmentation and classification of breast tumors are two critical tasks since they provide significant information for computer-aided breast cancer diagnosis...

DOI: 10.1007/s10278-025-01440-7

Yang X, Wang Y, Sui L

Journal of imaging informatics in medicine, 2025-02-21

The increasing role of artificial intelligence in radiation oncology: how should we navigate it?

У данной статьи нет абстракта.

DOI: 10.1007/s00066-025-02381-4

Putz F, Fietkau R

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2025-02-21

Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models.

Brain tumours are a significant health concern, often resulting in severe cognitive and physiological impairments...

DOI: 10.1016/j.compbiomed.2025.109872

Elhadidy MS, Elgohr AT, El-Geneedy M, Akram S, Kasem HM

Computers in biology and medicine, 2025-02-21

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.

PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients.

DOI: 10.1007/s11886-025-02215-w

Guha A, Shah V, Nahle T, Singh S, Kunhiraman HH, Shehnaz F, Nain P, Makram OM, Mahmoudi M, Al-Kindi S, Madabhushi A, Shiradkar R, Daoud H

Current cardiology reports, 2025-02-21

Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.

The deep learning model that integrates mpMRI and [18F]PSMA-1007 PET/CT enhances the predictive capabilities for the presence of AP in PCa patients. This improvement aids physicians in making informed preoperative decisions, ultimately enhancing patient prognosis.

DOI: 10.1007/s00259-025-07134-0

Lin H, Yao F, Yi X, Yuan Y, Xu J, Chen L, Wang H, Zhuang Y, Lin Q, Xue Y, Yang Y, Pan Z

European journal of nuclear medicine and molecular imaging, 2025-02-21

Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques.

We demonstrated that our deep learning model can consistently predict high quality VMAT lung plans for a variety of prescription doses. The dose prediction tool was also effective in identifying suboptimal plan quality, suggesting its potential benefit in automated treatment planning and evaluation.

DOI: 10.1002/mp.17692

Cao W, Gronberg M, Bilton S, Baroudi H, Gay S, Peeler C, Liao Z, Whitaker TJ, Hoffman K, Court LE

Medical physics, 2025-02-20

Enhancing automated right-sided early-stage breast cancer treatments via deep learning model adaptation without additional training.

The proposed technique adapts a DL model initially trained for LSB cancer for right-sided patients. It involves swapping the dose predictions from left to right and adjusting model settings, without the need for additional training. This technique-specific to a TPS-could be transposed to other TPS platforms.

DOI: 10.1002/mp.17682

Zeverino M, Fabiano S, Jeanneret-Sozzi W, Bourhis J, Bochud F, Moeckli R

Medical physics, 2025-02-20

Navigating advanced renal cell carcinoma in the era of artificial intelligence.

Artificial intelligence-enabled models have a great potential in improving clinical practice in the diagnosis and management of advanced renal cell carcinoma, particularly when developed from both clinicopathologic and radiologic data.

DOI: 10.1186/s40644-025-00835-7

Najem EJ, Shaikh MJS, Shinagare AB, Krajewski KM

Cancer imaging : the official publication of the International Cancer Imaging Society, 2025-02-20

G2M-checkpoint related immune barrier structure and signature for prognosis and immunotherapy response in hepatocellular carcinoma: insights from spatial transcriptome and machine learning.

This study highlights the importance of spatial heterogeneity and machine learning in refining HCC prognosis and ICIs response prediction. G2MRS and KPNA2 emerge as promising biomarkers for personalized HCC management.

DOI: 10.1186/s12967-024-06051-4

Chen X, Wu S, He H, Tang J, Zhong Y, Fang H, Huang Q, Hong L, Shao L, Wu J

Journal of translational medicine, 2025-02-20

Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.

The combined nomograms based on pre-intervention ultrasound radiomics and clinicopathological features demonstrated exemplary performance in survival analysis. The new models may allow us to non-invasively classify TNBC patients with various disease outcome.

DOI: 10.1186/s12885-025-13635-w

Wenwen , Jiang Z, Liu J, Liu D, Li Y, He Y, Zhao H, Ma L, Zhu Y, Long Q, Gao J, Luo H, Jiang H, Li K, Zhong X, Peng Y

BMC cancer, 2025-02-20

Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis.

This meta-analysis underscores the substantial diagnostic value of AI in identifying BM. Nevertheless, in-depth large-scale prospective research should be carried out for confirming AI's clinical utility in BM management.

DOI: 10.1186/s12885-025-13631-0

Tao H, Hui X, Zhang Z, Zhu R, Wang P, Zhou S, Yang K

BMC cancer, 2025-02-20

Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data.

A brain tumor is an abnormal growth of cells within the brain or surrounding tissues, which can be either benign or malignant...

DOI: 10.1038/s41598-025-89249-w

Khan F, Amanullah SI, Selvarajan S

Scientific reports, 2025-02-20

Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer.

Colorectal cancer (CRC) is the most common malignancy in the digestive system, with a lower 5-year overall survival rate...

DOI: 10.1038/s41598-025-89944-8

Wei H, Luo Q, Zhong W

Scientific reports, 2025-02-20

Democratizing cancer detection: artificial intelligence-enhanced endoscopy could address global disparities in head and neck cancer outcomes.

The latest data reflecting the growing disparity in HNSCC incidence and mortality across different regions and socioeconomic groups are reviewed, stressing the importance of early detection in improving outcomes. The potential role of AI in improving the accuracy and consistency of endoscopic procedures, particularly in resource-limited settings with limited access to specialized healthcare is discussed. By analyzing current technologies and case studies, the review highlights how AI can democratize access to early cancer detection, improve survival rates, and reduce the financial burden on patients. The integration of AI into healthcare systems can lead to significant cost savings and enhanced accessibility, particularly in underserved populations, where HNSCC is projected to rise dramatically in the coming decades.

DOI: 10.1007/s00405-025-09257-4

Sampieri C, Peretti G

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2025-02-20

Developing Effective Frameworks for Large Language Model-Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT.

This Viewpoint proposes a robust framework for developing a medical chatbot dedicated to radiotherapy education, emphasizing accuracy, reliability, privacy, ethics, and future innovations...

DOI: 10.2196/66633

Chow JCL, Li K

JMIR cancer, 2025-02-20

Leveraging natural language processing for efficient information extraction from breast cancer pathology reports: Single-institution study.

Our results showed that the NLP model, BioBERT, had higher accuracy than regular expression, suggesting the importance of BioBERT in the processing of breast cancer pathology reports.

DOI: 10.1371/journal.pone.0318726

Park P, Choi Y, Han N, Park YL, Hwang J, Chae H, Yoo CW, Choi KS, Kim HJ

PloS one, 2025-02-20

Genetic insights into the shared molecular mechanisms of Crohn's disease and breast cancer: a Mendelian randomization and deep learning approach.

The objective of this study was to explore the potential genetic link between Crohn's disease and breast cancer, with a focus on identifying druggable genes that may have therapeutic relevance...

DOI: 10.1007/s12672-025-01978-6

Wang ZZ, Yang JL, Zhang ZY, Wang PB

Discover oncology, 2025-02-20

Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning-based histomic clusters.

Deep learning-based histologic analysis effectively stratifies HGSC into clinically relevant prognostic groups, highlighting the role of mitochondrial dynamics and energy metabolism in disease progression. This method offers a novel approach to HGSC categorization.

DOI: 10.4132/jptm.2024.10.23

Ahn B, Park E

Journal of pathology and translational medicine, 2025-02-20

Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI.

A-DD images can improve the detectability of brain metastases without a significant loss of precision and could therefore represent a potentially valuable addition to regular single-dose brain imaging.

DOI: 10.1097/RLI.0000000000001166

Haase R, Pinetz T, Kobler E, Bendella Z, Zülow S, Schievelkamp AH, Schmeel FC, Panahabadi S, Stylianou AM, Paech D, Foltyn-Dumitru M, Wagner V, Schlamp K, Heussel G, Holtkamp M, Heussel CP, Vahlensieck M, Luetkens JA, Schlemmer HP, Haubold J, Radbruc

Investigative radiology, 2025-02-21

Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction.

Objective.State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included...

DOI: 10.1088/1361-6560/adb367

Klanecek Z, Wang YK, Wagner T, Cockmartin L, Marshall N, Schott B, Deatsch A, Studen A, Jarm K, Krajc M, Vrhovec M, Bosmans H, Jeraj R

Physics in medicine and biology, 2025-02-20

Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with continuous recurrence remaining an important clinical issue...

DOI: 10.1016/j.xcrm.2024.101924

Zhang H, Yang F, Xu Y, Zhao S, Jiang YZ, Shao ZM, Xiao Y

Cell reports. Medicine, 2025-02-21

Machine learning and multi-omics characterization of SLC2A1 as a prognostic factor in hepatocellular carcinoma: SLC2A1 is a prognostic factor in HCC.

Hepatocellular carcinoma (HCC) is characterized by high incidence, significant mortality, and marked heterogeneity, making accurate molecular subtyping essential for effective treatment...

DOI: 10.1016/j.gene.2024.149178

Xu K, Zhang H, Dai H, Mao W

Gene, 2025-02-22

Beyond the algorithm: ethical challenges in artificial intelligence-driven skin cancer diagnosis.

У данной статьи нет абстракта.

DOI: 10.1093/bjd/ljae490

Lynch M

The British journal of dermatology, 2025-02-20

Ethical implications of artificial intelligence in skin cancer diagnostics: use-case analyses.

Findings from our ethical use-case analysis of two patient-facing AI-assisted skin cancer apps suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps. Improved regulation should increase accountability. Ensuring ethics by design through integration between technology developers, dermatologists, ethicists and the public is urgently needed to prevent the potential benefits of AI-assisted skin cancer apps being overshadowed by potential ethical harms.

DOI: 10.1093/bjd/ljae434

Shah SFH, Arecco D, Draper H, Tiribelli S, Harriss E, Matin RN

The British journal of dermatology, 2025-02-20

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ИИ в онкологии

Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case-control study.

Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.

Validation of a digital pathology-based multimodal artificial intelligence biomarker in a prospective, real-world prostate cancer cohort treated with prostatectomy.

Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.

Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.

A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy.

Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer.

Development and internal validation of a predictive model of overall and progression-free survival in eribulin-treated patients with breast cancer based on baseline peripheral blood parameters.

Machine Learning-Driven Modeling to Predict Postdischarge Venous Thromboembolism After Pancreatectomy for Pancreas Cancer.

A predictive model for neoadjuvant therapy response in breast cancer.

Predicting hepatocellular carcinoma survival with artificial intelligence.

Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.

The Role of Artificial Intelligence in Gynecologic Oncology Decision Making: A Feasibility Study.

Validation of patient-specific deep learning markerless lung tumor tracking aided by 4DCBCT.

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

The puzzling Spitz tumours: is artificial intelligence the key to their understanding?

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.

Ensemble fuzzy deep learning for brain tumor detection.

NMTNet: A Multi-task Deep Learning Network for Joint Segmentation and Classification of Breast Tumors.

The increasing role of artificial intelligence in radiation oncology: how should we navigate it?

Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models.

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.

Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.

Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques.

Enhancing automated right-sided early-stage breast cancer treatments via deep learning model adaptation without additional training.

Navigating advanced renal cell carcinoma in the era of artificial intelligence.

G2M-checkpoint related immune barrier structure and signature for prognosis and immunotherapy response in hepatocellular carcinoma: insights from spatial transcriptome and machine learning.

Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.

Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis.

Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data.

Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer.

Democratizing cancer detection: artificial intelligence-enhanced endoscopy could address global disparities in head and neck cancer outcomes.

Developing Effective Frameworks for Large Language Model-Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT.

Leveraging natural language processing for efficient information extraction from breast cancer pathology reports: Single-institution study.

Genetic insights into the shared molecular mechanisms of Crohn's disease and breast cancer: a Mendelian randomization and deep learning approach.

Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning-based histomic clusters.

Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI.

Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction.

Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.

Machine learning and multi-omics characterization of SLC2A1 as a prognostic factor in hepatocellular carcinoma: SLC2A1 is a prognostic factor in HCC.

Beyond the algorithm: ethical challenges in artificial intelligence-driven skin cancer diagnosis.

Ethical implications of artificial intelligence in skin cancer diagnostics: use-case analyses.

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