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A deep learning-based peer review method for radiotherapy planning.

A deep learning dose prediction model based on patient information can be used to develop personalized QC in radiotherapy planning and can help improve the quality of radiotherapy plans.

DOI: 10.1002/mp.17686

Zhou P, Gu H, Peng Q, Kang D, Zhu J, Chen L

Medical physics, 2025-02-13

НОВАЯ СТАТЬЯ

A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers.

These findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers .

DOI: 10.1186/s12859-025-06051-1

Kennedy L, Sandhu JK, Harper ME, Cuperlovic-Culf M

BMC bioinformatics, 2025-02-13

НОВАЯ СТАТЬЯ

Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer.

The HRG signature holds the potential to serve as effective biomarkers for diagnosing, predicting the prognosis, and assessing the sensitivity to chemotherapy in PAAD.

DOI: 10.1007/s12672-025-01910-y

Li CL, Yao ZY, Qu C, Shao GM, Liu YK, Pei XY, Cao JY, Wang ZS

Discover oncology, 2025-02-13

НОВАЯ СТАТЬЯ

Establishment of a machine learning model for predicting splenic hilar lymph node metastasis.

Upper gastrointestinal cancer (UGC) sometimes metastasizes to the splenic hilum lymph node (SHLN)...

DOI: 10.1038/s41746-025-01480-x

Ishizu K, Takahashi S, Kouno N, Takasawa K, Takeda K, Matsui K, Nishino M, Hayashi T, Yamagata Y, Matsui S, Yoshikawa T, Hamamoto R

NPJ digital medicine, 2025-02-13

НОВАЯ СТАТЬЯ

RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

RAE-Net integrates feature fusion and uncertainty estimation to predict breast cancer subtypes from DCE-MRI. The model achieves high accuracy while maintaining computational efficiency, demonstrating its potential for clinical use as a reliable and resource-efficient diagnostic tool.

DOI: 10.1088/2057-1976/adb494

Tang XX, Zhu Y

Biomedical physics & engineering express, 2025-02-13

НОВАЯ СТАТЬЯ

Deep learning-assisted identification and localization of ductal carcinoma from bulk tissue in-silico models generated through polarized Monte Carlo simulations.

Despite significant progress in diagnosis and treatment, breast cancer remains a formidable health challenge, emphasizing the continuous need for research...

DOI: 10.1088/2057-1976/adb495

Ramkumar J, Sujatha N

Biomedical physics & engineering express, 2025-02-13

НОВАЯ СТАТЬЯ

ChatExosome: An Artificial Intelligence (AI) Agent Based on Deep Learning of Exosomes Spectroscopy for Hepatocellular Carcinoma (HCC) Diagnosis.

Large language models (LLMs) hold significant promise in the field of medical diagnosis...

DOI: 10.1021/acs.analchem.4c06677

Yang Z, Tian T, Kong J, Chen H

Analytical chemistry, 2025-02-13

НОВАЯ СТАТЬЯ

Deep Learning Radiomics Based on MRI for Differentiating Benign and Malignant Parapharyngeal Space Tumors.

The DLR model demonstrated a high predictive ability in diagnosing MTs and BTs of PPS and could serve as a powerful tool to aid clinical decision-making in the preoperative diagnosis of PPS tumors.

DOI: 10.1002/lary.32043

Yan H, Liu L, Xie M, Sun M, Yao J, Guo J, Li Y, Huang X, Huang D, Wang X, Qiu Y, Zhang X, Lu S, Liu Y

The Laryngoscope, 2025-02-13

НОВАЯ СТАТЬЯ

Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC) using CT-based radiomics...

DOI: 10.1007/s10916-025-02156-5

Le VH, Minh TNT, Kha QH, Le NQK

Journal of medical systems, 2025-02-13

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review.

Tumor size and TSH measurements were important in screening FTN malignancy risk preoperatively, but accurately predicting the risk of small-sized FTNs remains challenging. Future research should address the limitations posed by the extreme imbalance in FTA and FTC distributions in real-world data.

DOI: 10.2196/66269

Shan R, Li X, Chen J, Chen Z, Cheng YJ, Han B, Hu RZ, Huang JP, Kong GL, Liu H, Mei F, Song SB, Sun BK, Tian H, Wang Y, Xiao WC, Yao XY, Ye JM, Yu B, Yuan CH, Zhang F, Liu Z

JMIR cancer, 2025-02-12

Development of a deep learning system for predicting biochemical recurrence in prostate cancer.

Deep learning system can use biopsy samples to predict the risk of BCR in PCa, thereby formulating targeted treatment strategies.

DOI: 10.1186/s12885-025-13628-9

Cao L, He R, Zhang A, Li L, Cao W, Liu N, Zhang P

BMC cancer, 2025-02-12

An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: a multicenter study.

An interpretable, applicable, and generalized GNN model based on multi-center databases achieved superior performance in predicting ICT response and survival in LANPC patients, which may contribute to the personalized treatment of LANPC.

DOI: 10.1007/s00330-025-11396-5

Liao H, Zhao Y, Pei W, Huang X, Huang S, Wei W, Lai P, Jin W, Bao H, Liang X, Xiao L, Chen Z, Lu S, Su D, Lu B, Pan L

European radiology, 2025-02-12

A robust deep learning framework for multiclass skin cancer classification.

Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates...

DOI: 10.1038/s41598-025-89230-7

Ozdemir B, Pacal I

Scientific reports, 2025-02-12

Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

Local recurrences (LR) can occur within residual breast tissue, chest wall, skin, or newly formed scar tissue...

DOI: 10.1038/s41598-025-89339-9

Kovács KA, Kerepesi C, Rapcsák D, Madaras L, Nagy Á, Takács A, Dank M, Szentmártoni G, Szász AM, Kulka J, Tőkés AM

Scientific reports, 2025-02-12

Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Deep-learning networks using all Dixon images from a pre-contrast T1 sequence as multi-channel inputs produced the most clinically viable sCTs. Our proposed method may enable MR-only radiotherapy planning in a clinical setting for head and neck cancers.

DOI: 10.1016/j.radonc.2025.110782

Antunes J, Young T, Pittock D, Jacobs P, Nelson A, Piper J, Deshpande S

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2025-02-12

Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world...

DOI: 10.1080/07853890.2025.2461679

Lei C, Sun W, Wang K, Weng R, Kan X, Li R

Annals of medicine, 2025-02-12

Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.

For our OS evaluation, our JEPS fusion architecture achieves better integration of inputs and significantly improves predictive performance over most common multimodal approaches. The JEPS fusion technique is easily applied to any volumetric CNN.

DOI: 10.1002/mp.17672

Asbach JC, Singh AK, Iovoli AJ, Farrugia M, Le AH

Medical physics, 2025-02-12

Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis...

DOI: 10.1038/s41598-025-88753-3

Ahmed A, Sun G, Bilal A, Li Y, Ebad SA

Scientific reports, 2025-02-11

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Mammography is the recommended imaging modality for breast cancer screening...

DOI: 10.1038/s41598-024-83597-9

Zeng S, Chen H, Jing R, Yang W, He L, Zou T, Liu P, Liang B, Shi D, Wu W, Lin Q, Ma Z, Zha J, Zhong Y, Zhang X, Shao G, Gong P

Scientific reports, 2025-02-11

Development of machine learning prognostic models for overall survival of epithelial ovarian cancer patients: a seer-based study.

The GBSA model outperforms traditional methods in survival prediction, offering a valuable tool for clinicians to make informed decisions about patient prognosis.

DOI: 10.1080/14737140.2025.2465903

Fan J, Jiang Y, Wang X, Luv J

Expert review of anticancer therapy, 2025-02-11

Ultrasonic-Based Radiomics Signature With Machine Learning for Differentiating Prognostic Subsets of Pediatric Peripheral Neuroblastic Tumors: A Retrospective Study.

Our study proved that the radiomics model outperformed the combined model at differentiating prognostic subsets of pediatric neuroblastic tumors. Additionally, we found that high-dimensional ultrasonic-based radiomics features surpassed other features and clinical characteristics.

DOI: 10.1016/j.ultrasmedbio.2025.01.013

Zhu H, Ye J, Luo H, Ni S, Pan Y, Zhao Z, Yang Y

Ultrasound in medicine & biology, 2025-02-11

Response to the comment on "Deep convolutional neural network for automatic segmentation and classification of jaw tumors in contrast-enhanced computed tomography images".

У данной статьи нет абстракта.

DOI: 10.1016/j.ijom.2025.01.019

Warin K, Limprasert W, Paipongna T, Chaowchuen S, Vicharueang S

International journal of oral and maxillofacial surgery, 2025-02-11

ChatGPT-4o's Performance in Brain Tumor Diagnosis and MRI Findings: A Comparative Analysis with Radiologists.

ChatGPT-4o demonstrated high accuracy in identifying certain MRI features but underperformed in diagnostic tasks in comparison with the radiologists. Despite its current limitations, future updates and advancements have the potential to enable large language models to facilitate diagnosis and offer a reliable second opinion to radiologists.

DOI: 10.1016/j.acra.2025.01.033

Ozenbas C, Engin D, Altinok T, Akcay E, Aktas U, Tabanli A

Academic radiology, 2025-02-11

Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

Our study provided a method of HER2 status recognition based on IHC images, improving the efficiency and accuracy of HER2 status assessment, and can be used for predicting the efficacy of anti-HER2 targeted neoadjuvant therapy. We intend our deep learning model to assist pathologists in HER2 amplification recognition.

DOI: 10.1007/s00432-025-06105-0

Wang J, Gao G, Tian C, Zhang J, Jiao DC, Liu ZZ

Journal of cancer research and clinical oncology, 2025-02-11

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies...

DOI: 10.1038/s41467-025-56783-0

Josephides JM, Chen CL

Nature communications, 2025-02-10

The use of large language models to enhance cancer clinical trial educational materials.

LLMs such as GPT4 show promise in generating patient-friendly educational content for clinical trials with minimal trial-specific engineering. The findings serve as a proof-of-concept for the role of LLMs in improving patient education and engagement in clinical trials, as well as the need for ongoing human oversight.

DOI: 10.1093/jncics/pkaf021

Gao M, Varshney A, Chen S, Goddla V, Gallifant J, Doyle P, Novack C, Dillon-Martin M, Perkins T, Correia X, Duhaime E, Isenstein H, Sharon E, Lehmann LS, Kozono D, Anthony B, Dligach D, Bitterman DS

JNCI cancer spectrum, 2025-02-10

Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.

Breast cancer is a leading cause of mortality among women, with recurrence prediction remaining a significant challenge...

DOI: 10.1007/s12672-025-01908-6

Silveira JA, da Silva AR, de Lima MZT

Discover oncology, 2025-02-10

Machine Learning: A Multicentre Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.

ML, especially RF, reliably predicts lateral LNM in cN0 PTC patients, outperforming traditional nomograms.

DOI: 10.1210/clinem/dgaf070

Zhou J, Li D, Ren J, Huang C, Yang S, Chen M, Wan Z, He J, Zhuang Y, Xue S, Chun L, Su X

The Journal of clinical endocrinology and metabolism, 2025-02-10

Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer.

The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.

DOI: 10.1080/1354750X.2025.2461698

Shah SNA, Parveen R

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals, 2025-02-12

Corrigendum to "Artificial intelligence-driven hydrogel microneedle patches integrating 5-fluorouracil inclusion complex-loaded flexible pegylated liposomes for enhanced non-melanoma skin cancer treatment" [Int. J. Pharm. 669 (2025) 125072].

У данной статьи нет абстракта.

DOI: 10.1016/j.ijpharm.2025.125260

Suriyaamporn P, Pornpitchanarong C, Charoenying T, Dechsri K, Ngawhirunpat T, Opanasopit P, Pamornpathomkul B

International journal of pharmaceutics, 2025-02-12

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ИИ в онкологии

A deep learning-based peer review method for radiotherapy planning.

A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers.

Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer.

Establishment of a machine learning model for predicting splenic hilar lymph node metastasis.

RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Deep learning-assisted identification and localization of ductal carcinoma from bulk tissue in-silico models generated through polarized Monte Carlo simulations.

ChatExosome: An Artificial Intelligence (AI) Agent Based on Deep Learning of Exosomes Spectroscopy for Hepatocellular Carcinoma (HCC) Diagnosis.

Deep Learning Radiomics Based on MRI for Differentiating Benign and Malignant Parapharyngeal Space Tumors.

Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review.

Development of a deep learning system for predicting biochemical recurrence in prostate cancer.

An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: a multicenter study.

A robust deep learning framework for multiclass skin cancer classification.

Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.

Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.

Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Development of machine learning prognostic models for overall survival of epithelial ovarian cancer patients: a seer-based study.

Ultrasonic-Based Radiomics Signature With Machine Learning for Differentiating Prognostic Subsets of Pediatric Peripheral Neuroblastic Tumors: A Retrospective Study.

Response to the comment on "Deep convolutional neural network for automatic segmentation and classification of jaw tumors in contrast-enhanced computed tomography images".

ChatGPT-4o's Performance in Brain Tumor Diagnosis and MRI Findings: A Comparative Analysis with Radiologists.

Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

The use of large language models to enhance cancer clinical trial educational materials.

Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.

Machine Learning: A Multicentre Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.

Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer.

Corrigendum to "Artificial intelligence-driven hydrogel microneedle patches integrating 5-fluorouracil inclusion complex-loaded flexible pegylated liposomes for enhanced non-melanoma skin cancer treatment" [Int. J. Pharm. 669 (2025) 125072].

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