Early identification of unresectable hepatocellular carcinoma (HCC) patients who may benefit from immune checkpoint inhibitors (ICIs) is crucial for optimizing outcomes. Here, we developed a multimodal fusion (MMF) system integrating CT-derived deep learning features and clinical data to predict overall survival (OS) and progression-free survival (PFS). Using retrospective multicenter data (n = 859), the MMF combining an ensemble deep learning (Ensemble-DL) model with clinical variables achieved strong external validation performance (C-index: OS = 0.74, PFS = 0.69), outperforming radiomics (29.8% OS improvement), mRECIST (27.6% OS improvement), clinical benchmarks (C-index: OS = 0.67, p = 0.0011; PFS = 0.65, p = 0.033), and Ensemble-DL (C-index: OS = 0.69, p = 0.0028; PFS = 0.66, p = 0.044). The MMF system effectively stratified patients across clinical subgroups and demonstrated interpretability through activation maps and radiomic correlations. Differential gene expression analysis revealed enrichment of the PI3K/Akt pathway in patients identified by the MMF system. The MMF system provides an interpretable, clinically applicable approach to guide personalized ICI treatment in unresectable HCC.

The aim of this study was to explore and develop a preoperative and noninvasive model for predicting spread through air spaces (STAS) status in lung adenocarcinoma (LUAD) with diameter ≤ 3 cm.

This multicenter retrospective study included 640 LUAD patients. Center I included 525 patients (368 in the training cohort and 157 in the validation cohort); center II included 115 patients (the test cohort). We extracted radiomics features from the intratumor, extended tumor and peritumor regions. Multivariate logistic regression and boruta algorithm were used to select clinical independent risk factors and radiomics features, respectively. We developed a clinical model and four radiomics models (the intratumor model, extended tumor model, peritumor model and fusion model). A nomogram based on prediction probability value of the optimal radiomics model and clinical independent risk factors was developed to predict STAS status.

Maximum diameter and nodule type were clinical independent risk factors. The extended tumor model achieved satisfactory STAS status discrimination performance with the AUC of 0.74, 0.71 and 0.80 in the three cohorts, respectively, performed better than other radiomics models. The integrated discrimination improvement value revealed that the nomogram outperformed compared to the clinical model with the value of 12 %. Patients with high nomogram score (≥ 77.31) will be identified as STAS-positive.

Peritumoral information is significant to predict STAS status. The nomogram based on the extended tumor model and clinical independent risk factors provided good preoperative prediction of STAS status in LUAD with diameter ≤ 3 cm, aiding surgical decision-making.

Radiation-induced esophagitis remains a significant challenge in thoracic and neck cancer treatment, impacting patient quality of life and potentially limiting therapeutic efficacy. This study aimed to develop and validate a decision tree-based model for predicting acute esophagitis grades in patients undergoing chemoradiotherapy.

Data from 100 patients receiving thoracic and neck radiotherapy were analyzed. The dataset comprised 33 features, including demographic, clinical, and dosimetric parameters. A decision tree classifier was implemented for both binary (Grade ≥2 vs. <2) and multi-class (Grades 1, 2, and 3) classification. Model performance was evaluated using standard metrics including accuracy, precision, recall, and F1-score.

The binary classification model achieved 97 % accuracy in distinguishing acute esophagitis. The multi-class model demonstrated 98 % accuracy in predicting specific grades. Key predictive features included V40 (volume receiving 40 Gy), V60, and average esophageal dose. The model generated interpretable decision rules, with V60 ≥ 2.3 strongly indicating Grade 3 esophagitis.

The decision tree model demonstrates high accuracy in predicting radiation-induced esophagitis grades while maintaining clinical interpretability. This approach offers potential for treatment optimization and personalized risk assessment in radiotherapy planning. The model's transparency and reliability make it a promising tool for clinical decision support in radiation oncology.

Persistent infection with human papillomavirus (HPV) can lead to cervical cancer (CC), which is the fourth most commonly diagnosed cancer in women globally. In this review, we have explained the HPV genome and the development of CC. Additionally, we summarized recently discovered small molecules that act as inhibitors of HPV-16. These molecules were identified through experimental and in-silico studies aimed at preventing or treating CC. HPV-16 and HPV-18 are the most common subtypes of HPV that cause CC globally. E6 oncoprotein of HPV-16 is considered the primary cause of CC progression. Therefore, E6 is the most focused targeted protein for developing specific and novel therapeutic inhibitors to treat HPV-related cancers. In recent years, various HPV inhibitors have been identified by means of experimental and in-silico studies. In addition, artificial intelligence-based medical diagnostic tools have grown more popular as they are capable of screening and diagnosing HPV-related cancer.

Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized.

This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions.

AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges.

AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.

Artificial intelligence (AI) is rapidly transforming pediatric oncology by creating new means to improve the accuracy and efficacy of cancer diagnosis and treatment in children. This review critically examines current applications of AI technologies like machine learning (ML) and deep learning (DL) to the main types of pediatric cancers. However, the application of AI to pediatric oncology is prone to certain challenges, including the heterogeneity and rarity of pediatric cancer data, rapid technological development in imaging, and ethical concerns pertaining to data privacy and algorithmic transparency. Collaborative efforts and data-sharing schemes are important to surpass these challenges and facilitate effective training of AI models. This review also points to emerging trends, including AI-based radiomics and proteomics applications, and provides future directions to realize the full potential of AI in pediatric oncology. Finally, AI is a promising paradigm shift toward precision medicine in childhood cancer treatment, which can enhance the survival rates and quality of life for pediatric patients.

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation (e.g., Osteosarcoma vs. Ewing sarcoma), and predicting mutation signatures (e.g., TP53, RB1). Moreover, we highlight the use of liquid biopsy and circulating tumor DNA (ctDNA) as emerging diagnostic biomarkers, coupled with point-of-care molecular assays, to enable early and accurate detection in low-resource settings. The review concludes by discussing translational barriers, including data harmonization, regulatory challenges, and the need for multi-institutional datasets to validate AI-based diagnostic frameworks. This article synthesizes current advancements and provides a forward-looking view of precision diagnostics in musculoskeletal oncology.

Iriscope, a 1.3 mm video endoscopic probe introduced through an r-EBUS catheter, allows for the direct visualisation of small peripheral pulmonary nodules (PPNs). This study assessed the ability of physicians with different levels of experience in bronchoscopy, and the ability of artificial intelligence (AI) to predict the malignant nature of small PPNs during Iriscope peripheral endoscopy.

Patients undergoing bronchoscopy with r-EBUS and Iriscope for peripheral PPNs < 20 mm with a definite diagnosis were analysed. Senior and Junior physicians independently interpreted video-recorded Iriscope sequences, classifying them as tumoral (malignant) or non-tumoral, blind to the final diagnosis. A deep learning (DL) model was also trained on Iriscope images and tested on a different set of patients for comparison with human interpretation. Diagnostic accuracy, sensitivity, specificity, and F1 score were calculated.

Sixty-one patients with small PPNs (median size 15 mm, IQR: 11-20 mm) were included. The technique allowed for the direct visualisation of the lesions in all cases. The final diagnosis was cancer for 37 cases and a benign lesion in 24 cases. Senior physicians outperformed junior physicians in recognising tumoral Iriscope images, with a balanced accuracy of 85.4% versus 66.7%, respectively, when compared with the final diagnosis. The DL model outperformed junior physicians with a balanced accuracy of 71.5% but was not superior to senior physicians.

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.

This study is aimed to develop predictive models for classifying thymic epithelial tumor (TET) histological subtypes (A/AB/B1, B2/B3, C) and WHO stages (I-IV) using radiomics features derived from contrast-enhanced CT scans. These models were validated on multicenter external datasets to improve preoperative diagnosis and guide treatment decisions. A total of 257 patients diagnosed with TET between January 2013 and April 2024 were retrospectively analyzed, with 181 cases from the First Affiliated Hospital of Soochow University served as the training cohort and 76 cases from the Second Affiliated Hospital used as an external test set. All patients underwent preoperative enhanced CT scans. After manual segmentation of the volume of interest (VOI), 1,038 radiomic features were extracted. Feature selection was performed using PCA and LASSO methods. Three models (clinical semantic, radiomics, and a fusion model combining both) were built using random forest algorithms. The fusion model achieved the highest performance in the external test set, with an accuracy of 0.908 and F1 score of 0.896 for histological subtype classification, and an accuracy of 0.803 and F1 score of 0.833 for WHO staging. The radiomics model shows slightly lower performance, while the clinical semantic model performs the weakest. Our findings suggest that machine learning models integrating radiomics and clinical features can effectively predict TET subtypes and stages, offering a non-invasive tool for accurate preoperative assessment with strong generalization ability.

Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).

The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort.

Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.

The use of Artificial Intelligence (AI) is exponentially rising in the healthcare sector. This change influences various domains of early identification, diagnosis, and treatment of diseases.

This study examines the integration of AI in healthcare, focusing on its transformative potential in diagnostics and treatment, and the challenges and methodologies. shaping its future development.

The review included 68 academic studies retracted from different databases (WOS, Scopus and Pubmed) from January 2020 and April 2024. After careful review and data analysis, AI methodologies, benefits and challenges, were summarized.

The number of studies showed a steady rise from 2020 to 2023. Most of them were the results of a collaborative work with international universities (92.1%). The majority (66.7%) were published in top-tier (Q1) journals and 40% were cited 2-10 times. The results have shown that AI tools such as deep learning methods and machine learning continue to significantly improve accuracy and timely execution of medical processes. Benefits were discussed from both the organizational and the patient perspective in the categories of diagnosis, treatment, consultation and health monitoring of diseases. However, some challenges may exist, despite these benefits, and are related to data integration, errors related to data processing and decision making, and patient safety.

The article examines the present status of AI in medical applications and explores its potential future applications. The findings of this review are useful for healthcare professionals to acquire deeper knowledge on the use of medical AI from design to implementation stage. However, a thorough assessment is essential to gather more insights into whether AI benefits outweigh its risks. Additionally, ethical and privacy issues need careful consideration.

Background: Survival prediction in patients with brain metastases remains a major clinical challenge, where timely and individualized prognostic estimates are critical for guiding treatment strategies and patient counseling. Methods: We propose a novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers with structured clinical and demographic data to predict overall survival time. Our dataset includes 148 patients from three institutions, featuring expert-annotated segmentations of enhancing tumors, necrosis, and peritumoral edema. Two convolutional neural network backbones-ResNet-50 and EfficientNet-B0-were fused with fully connected layers processing tabular data. Models were trained using mean squared error loss and evaluated through stratified cross-validation and an independent held-out test set. Results: The hybrid model based on EfficientNet-B0 achieved state-of-the-art performance, attaining an R2 score of 0.970 and a mean absolute error of 3.05 days on the test set. Permutation feature importance highlighted edema-to-tumor ratio and enhancing tumor volume as the most informative predictors. Grad-CAM visualizations confirmed the model's attention to anatomically and clinically relevant regions. Performance consistency across validation folds confirmed the framework's robustness and generalizability. Conclusions: This study demonstrates that multimodal deep learning can deliver accurate, explainable, and clinically actionable survival predictions in brain metastases. The proposed framework offers a promising foundation for integration into real-world oncology workflows to support personalized prognosis and informed therapeutic decision-making.

Accurately evaluating human epidermal growth factor receptor (HER2) expression status in breast cancer enables clinicians to develop individualized treatment plans and improve patient prognosis. The purpose of this study was to assess the performance of a machine learning (ML) model that was developed using 18F-FDG PET/CT parameters and clinicopathological features in distinguishing different levels of HER2 expression in breast cancer.

This retrospective study enrolled breast cancer patients who underwent 18F-FDG PET/CT scans prior to treatment at Lianyungang First People's Hospital (centre 1, n=157) and the Third Affiliated Hospital of Soochow University (centre 2, n=84). Two classification tasks were analysed: distinguishing HER2-zero expression from HER2-low/positive expression (Task 1) and distinguishing HER2-low expression from HER2-positive expression (Task 2). For each task, patients from Centre 1 were randomly divided into training and internal test sets at a 7:3 ratio, whereas patients from Centre 2 served as an external test set. The prediction models included logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost) and multilayer perceptron (MLP), and SHAP analysis provided model interpretability. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

XGBoost models exhibited the best predictive performance in both tasks. For Task 1, recursive feature elimination (RFE) was used to select 8 features, excluding pathological features, and the XGBoost model achieved AUCs of 0.888, 0.844 and 0.759 for the training, internal and external testing sets, respectively. The top three features according to the SHAP values were the tumour minimum diameter, mean standardized uptake value (SUVmean) and CTmean. For Task 2, 9 features were selected, including progesterone receptor (PR) status as a pathological feature. The XGBoost model achieved AUCs of 0.920, 0.814 and 0.693 for the training, internal and external testing sets, respectively. The top three features according to the SHAP values were the PR status, maximum tumour diameter and metabolic tumour volume (MTV).

ML models that incorporate 18F-FDG PET/CT parameters and clinicopathological features can aid in the prediction of different HER2 expression statuses in breast cancer.

To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict pathological grade and Ki67 index in clear cell renal cell carcinoma (ccRCC).

In this multicenter retrospective study, a total of 1073 pathologically confirmed ccRCC patients from seven cohorts were split into internal cohorts (training and validation sets) and an external test set. The AI framework comprised an image processor, a 3D-kidney and tumor segmentation model by 3D-UNet, a multi-scale features extractor built upon unsupervised learning, and a multi-task classifier utilizing XGBoost. A quantitative model interpretation technique, known as SHapley Additive exPlanations (SHAP), was employed to explore the contribution of multi-scale features.

The 3D-UNet model showed excellent performance in segmenting both the kidney and tumor regions, with Dice coefficients exceeding 0.92. The proposed multi-scale features model exhibited strong predictive capability for pathological grading and Ki67 index, with AUROC values of 0.84 and 0.87, respectively, in the internal validation set, and 0.82 and 0.82, respectively, in the external test set. The SHAP results demonstrated that features from radiomics, the 3D Auto-Encoder, and dimensionality reduction all made significant contributions to both prediction tasks.

The proposed AI framework, leveraging multi-scale features, accurately predicts the pathological grade and Ki67 index of ccRCC.

The CT-based AI framework leveraging multi-scale features offers a promising avenue for accurately predicting the pathological grade and Ki67 index of ccRCC preoperatively, indicating a direction for non-invasive assessment.

Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. The AI framework enables non-invasive preoperative detection of high-risk tumors, assisting clinical decision-making.

Retroperitoneal sarcoma (RPS) is highly heterogeneous, leading to different risks of distant metastasis (DM) among patients with the same clinical stage. This study aims to develop a quantitative method for assessing intratumoral heterogeneity (ITH) using preoperative contrast-enhanced CT (CECT) scans and evaluate its ability to predict DM risk.

We conducted a retrospective analysis of 274 PRS patients who underwent complete surgical resection and were monitored for ≥ 36 months at two centers. Conventional radiomics (C-radiomics), ITH radiomics, and deep-learning (DL) features were extracted from the preoperative CECT scans and developed single-modality models. Clinical indicators and high-throughput CECT features were integrated to develop a combined model for predicting DM. The performance of the models was evaluated by measuring the receiver operating characteristic curve and Harrell's concordance index (C-index). Distant metastasis-free survival (DMFS) was also predicted to further assess survival benefits.

The ITH model demonstrated satisfactory predictive capability for DM in internal and external validation cohorts (AUC: 0.735, 0.765; C-index: 0.691, 0.729). The combined model that combined clinicoradiological variables, ITH-score, and DL-score achieved the best predictive performance in internal and external validation cohorts (AUC: 0.864, 0.801; C-index: 0.770, 0.752), successfully stratified patients into high- and low-risk groups for DM (p < 0.05).

The combined model demonstrated promising potential for accurately predicting the DM risk and stratifying the DMFS risk in RPS patients undergoing complete surgical resection, providing a valuable tool for guiding treatment decisions and follow-up strategies.

The intratumoral heterogeneity analysis facilitates the identification of high-risk retroperitoneal sarcoma patients prone to distant metastasis and poor prognoses, enabling the selection of candidates for more aggressive surgical and post-surgical interventions.

Preoperative identification of retroperitoneal sarcoma (RPS) with a high potential for distant metastasis (DM) is crucial for targeted interventional strategies. Quantitative assessment of intratumoral heterogeneity achieved reasonable performance for predicting DM. The integrated model combining clinicoradiological variables, ITH radiomics, and deep-learning features effectively predicted distant metastasis-free survival.

Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical Diagnosis Model (PTs-HDM) for preoperative identification and grading.

Ultrasound images from five hospitals were retrospectively collected, with all patients having undergone surgical pathological confirmation of either PTs or fibroadenomas (FAs). PTs-HDM follows a two-stage classification: first distinguishing PTs from FAs, then grading PTs into benign or borderline/malignant. Model performance metrics including AUC and accuracy were quantitatively evaluated. A comparative analysis was conducted between the algorithm's diagnostic capabilities and those of radiologists with varying clinical experience within an external validation cohort. Through the provision of PTs-HDM's automated classification outputs and associated thermal activation mapping guidance, we systematically assessed the enhancement in radiologists' diagnostic concordance and classification accuracy.

A total of 712 patients were included. On the external test set, PTs-HDM achieved an AUC of 0.883, accuracy of 87.3% for PT vs. FA classification. Subgroup analysis showed high accuracy for tumors < 2 cm (90.9%). In hierarchical classification, the model obtained an AUC of 0.856 and accuracy of 80.9%. Radiologists' performance improved with PTs-HDM assistance, with binary classification accuracy increasing from 82.7%, 67.7%, and 64.2-87.6%, 76.6%, and 82.1% for senior, attending, and resident radiologists, respectively. Their hierarchical classification AUCs improved from 0.566 to 0.827 to 0.725-0.837. PTs-HDM also enhanced inter-radiologist consistency, increasing Kappa values from - 0.05 to 0.41 to 0.12 to 0.65, and the intraclass correlation coefficient from 0.19 to 0.45.

PTs-HDM shows strong diagnostic performance, especially for small lesions, and improves radiologists' accuracy across all experience levels, bridging diagnostic gaps and providing reliable support for PTs' hierarchical diagnosis.

Artificial intelligence (AI) is a promising tool used in oncology that may be able to facilitate diagnosis, treatment planning, and patient management. Transparency and completeness of protocols of randomized controlled trials (RCT) involving AI interventions is necessary to ensure reproducibility of AI tools across diverse clinical settings. The SPIRIT 2013 and SPIRIT-AI 2020 guidelines were developed as evidence-based recommendations for complete reporting of trial protocols. However, the concordance of AI RCT protocols in oncology to SPIRIT reporting guidelines remains unknown. This systematic review evaluates the concordance of protocols of RCTs evaluating AI interventions in oncology to the SPIRIT 2013 and SPIRIT-AI 2020 reporting guidelines.

A systematic search of Ovid Medline and Embase was conducted on October 22, 2024 for primary, peer-reviewed RCT protocols involving AI interventions in oncology. Eligible studies were screened in duplicate and data extraction assessed concordance to SPIRIT 2013 and SPIRIT-AI 2020 guideline items. Item-specific concordance was measured as the proportion of studies that reported the item. Average concordance was measured as the median proportion of items reported for each study.

Twelve RCT protocols met the inclusion criteria. The median concordance to SPIRIT 2013 guidelines was 81.92% (IQR 74.88-88.95) and the median concordance to SPIRIT-AI 2020 guidelines was 78.21% (IQR 67.21-89.20). For SPIRIT 2013 guidelines, high concordance was observed for items related to study objectives and ethics, but gaps were identified in reporting blinding procedures, participant retention, and post-trial care. For SPIRIT-AI 2020 guidelines, there remained gaps based on data quality management, performance error analysis, and accessibility of AI intervention code.

While concordance to reporting guidelines in oncology AI RCT protocols was moderately high, critical gaps in protocol reporting persist that may hinder reproducibility and clinical implementation. Future efforts should focus on increasing awareness and reinforcement to enhance reporting quality necessary to foster the responsible integration of AI into oncology practice.

The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The intrinsic creative ability of artificial intelligence (AI) offers improvements in patient screening, diagnosis, and individualized care. Advanced technologies, like computer vision, machine learning, deep learning, and natural language processing, can analyze large datasets and identify patterns that permit early cancer detection, diagnosis, management and incorporation of conclusive treatment plans, ensuring improved quality of life for patients by personalizing care and minimizing unnecessary interventions. Genomics, transcriptomics and proteomics data can be combined with AI algorithms to unveil an extensive overview of cancer biology, assisting in its detailed understanding and will help in identifying new drug targets and developing effective therapies. This can also help to identify personalized molecular signatures which can facilitate tailored interventions addressing the unique aspects of each patient. AI-driven transcriptomics, proteomics, and genomes represents a revolutionary strategy to improve patient outcome by offering precise diagnosis and tailored therapy. The inclusion of AI in oncology may boost efficiency, reduce errors, and save costs, but it cannot take the role of medical professionals. While clinicians and doctors have the final say in all matters, it might serve as their faithful assistant.

Malnutrition is a critical complication among cancer patients, affecting ≤80% of individuals depending on cancer type, stage, and treatment. Artificial intelligence (AI) has emerged as a promising tool in healthcare, with potential applications in nutritional management to improve early detection, risk stratification, and personalized interventions. This systematic review evaluated the role of AI in identifying and managing malnutrition in cancer patients, focusing on its effectiveness in nutritional status assessment, prediction, clinical outcomes, and body composition monitoring. A systematic search was conducted across PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica Database from June to July 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quantitative primary studies investigating AI-based interventions for malnutrition detection, body composition analysis, and nutritional optimization in oncology were included. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools, and evidence certainty was evaluated with the Oxford Centre for Evidence-Based Medicine framework. Eleven studies (n = 52,228 patients) met the inclusion criteria and were categorized into 3 overarching domains: nutritional status assessment and prediction, clinical and functional outcomes, and body composition and cachexia monitoring. AI-based models demonstrated high predictive accuracy in malnutrition detection (area under the curve >0.80). Machine learning algorithms, including decision trees, random forests, and support vector machines, outperformed conventional screening tools. Deep learning models applied to medical imaging achieved high segmentation accuracy (Dice similarity coefficient: 0.92-0.94), enabling early cachexia detection. AI-driven virtual dietitian systems improved dietary adherence (84%) and reduced unplanned hospitalizations. AI-enhanced workflows streamlined dietitian referrals, reducing referral times by 2.4 d. AI demonstrates significant potential in optimizing malnutrition screening, body composition monitoring, and personalized nutritional interventions for cancer patients. Its integration into oncology nutrition care could enhance patient outcomes and optimize healthcare resource allocation. Further research is necessary to standardize AI models and ensure clinical applicability. This systematic review followed a protocol registered prospectively on Open Science Framework (https://doi.org/10.17605/OSF.IO/A259M).

Intratumoral heterogeneity (ITH) is a critical factor associated with treatment failure and disease relapse in hepatocellular carcinoma (HCC). However, decoding ITH in a noninvasive and comprehensive manner remains a notable challenge. In this study involving 851 patients from five centers, we developed a noninvasive prognostic classification for ITH using radiomics based on multisequence MRI, termed radiomics ITH (RITH) phenotypes. The RITH phenotypes highly correlated with prognosis and pathological ITH. In addition, through an integrated multi-omics analysis, we uncovered the molecular mechanisms underlying RITH, notably enhancing its biological interpretability. Specifically, high-RITH tumors demonstrated an enrichment of cancer-associated fibroblasts and activation of extracellular matrix remodeling. Our approach facilitates the noninvasive refined classification of ITH using radiomics and multi-omics, paving the way for tailored treatment strategies in HCC. Extracellular matrix-receptor interaction could be a potential therapeutic target in patients with high-RITH tumors. Given the routine use of radiologic imaging in oncology, our methodology ignites versatile framework for broader application to other solid tumors.

Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.

Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta-analysis of the diagnostic accuracy, specificity, sensitivity, and negative and positive predictive values of AI-assisted CE in the diagnosis of small bowel lesions in comparison to CE.

Literature searches were performed through PubMed, SCOPUS, and EMBASE to identify studies eligible for inclusion. All publications up to 24 November 2024 were included. Original articles (including observational studies and randomized control trials), systematic reviews, meta-analyses, and case series reporting outcomes on AI-assisted CE in the diagnosis of small bowel lesions were included. The extracted data were pooled, and a meta-analysis was performed for the appropriate variables, considering the clinical and methodological heterogeneity among the included studies. Comprehensive Meta-Analysis v4.0 (Biostat Inc.) was used for the analysis of the data.

A total of 14 studies were included in the present study. The mean age of participants across the studies was 54.3 years (SD 17.7), with 55.4% men and 44.6% women. The pooled accuracy for conventional CE was 0.966 (95% CI: 0.925-0.988), whereas for AI-assisted CE, it was 0.9185 (95% CI: 0.9138-0.9233). Conventional CE exhibited a pooled sensitivity of 0.860 (95% CI: 0.786-0.934) compared with AI-assisted CE at 0.9239 (95% CI: 0.8648-0.9870). The positive predictive value for conventional CE was 0.982 (95% CI: 0.976-0.987), whereas AI-assisted CE had a PPV of 0.8928 (95% CI: 0.7554-0.999). The pooled specificity for conventional CE was 0.998 (95% CI: 0.996-0.999) compared with 0.5367 (95% CI: 0.5244-0.5492) for AI-assisted CE. Negative predictive values were higher in AI-assisted CE at 0.9425 (95% CI: 0.9389-0.9462) versus 0.760 (95% CI: 0.577-0.943) for conventional CE.

AI-assisted CE displays superior diagnostic accuracy, sensitivity, and positive predictive values albeit the lower pooled specificity in comparison with conventional CE. Its use would ensure accurate detection of small bowel lesions and further enhance their management.

Immunotherapy emerges as a promising approach, marked by recent substantial progress in elucidating how the host immune response impacts tumor development and its sensitivity to various treatments. Immune checkpoint inhibitors have revolutionized cancer therapy by unleashing the power of the immune system to recognize and eradicate tumor cells. Among these, inhibitors targeting the programmed cell death protein 1 (PD-1) and its ligand (PD-L1) have garnered significant attention due to their remarkable clinical efficacy across various malignancies. This review delves into the mechanisms of action, clinical applications, and emerging therapeutic strategies surrounding PD-1/PD-L1 blockade. We explore the intricate interactions between PD-1/PD-L1 and other immune checkpoints, shedding light on combinatorial approaches to enhance treatment outcomes and overcome resistance mechanisms. Furthermore, we discuss the expanding landscape of immune checkpoint inhibitors beyond PD-1/PD-L1, including novel targets such as CTLA-4, LAG-3, TIM-3, and TIGIT. Through a comprehensive analysis of preclinical and clinical studies, we highlight the promise and challenges of immune checkpoint blockade in cancer immunotherapy, paving the way for future advancements in the field.

Radiomics, an advanced medical imaging analysis technique introduced by Professor Lambin in 2012, has quickly become a key area of medical research. To explore emerging trends in cancer-related radiomics, we conducted a bibliometric analysis of the 100 most-cited articles (T100) in this field. Data were collected from the Web of Science Core Collection on October 7, 2023, and the articles were ranked by citation count. We extracted data such as authors, journals, citation counts, and publication years and analyzed it using Microsoft Excel 2019 and R 4.4.2. CiteSpace was used to create co-occurrence and citation burst maps to show the relationships between authors, countries, institutions, and keywords. The analysis revealed that the T100 came from 81 countries, with the U.S. contributing the most (56 articles). Harvard University was the leading institution, and the journal Radiology had the highest citation count. Aerts Hugo JWL was the most influential author. The study highlights that "lung cancer" and "artificial intelligence" are emerging as major research hotspots, shaping the future of cancer radiomics.

A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018-2024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies.

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC's heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in precision diagnosis, prognosis prediction, and biomarker discovery for achieving personalized medicine.

The immunotherapy landscape for gastrointestinal (GI) tumors is rapidly evolving. There is an urgent need for reliable biomarkers capable of predicting treatment outcomes to optimize therapeutic strategies and enhance patient prognosis. This review presents a comprehensive overview of biomarkers associated with the immunotherapy response of GI tumors, covering advances in molecular genetics, histopathological markers, and imaging. Key molecular biomarkers, such as microsatellite instability, tumor mutational burden, and programmed death-ligand 1 expression, remain critical for identifying patients likely to benefit from immune checkpoint inhibitors. The significance of tumor-infiltrating lymphocytes, notably the CD8+ T cell to regulatory T cell ratio, as a predictor of immunotherapy response is explored. In addition, advanced imaging techniques, including computed tomography (CT), magnetic resonance imaging, and positron emission tomography-CT, facilitate the noninvasive evaluation of tumor biology and therapeutic response. By bridging molecular and imaging data, this integrated strategy enhances precision in patient selection, treatment monitoring, and adaptive therapy design. Future studies should aim to validate these biomarkers in larger, multicenter cohorts and focus on clinical translation to advance precision medicine in GI oncology.