Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs). Methods: We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS–PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by the Random Forest algorithm) to 199 LCa cases, using the same HMLS techniques. Furthermore, principal component analysis (PCA) linked with four survival prediction algorithms were utilized in the survival hazard ratio analysis. Results: The SSL strategy outperformed the SL method (p << 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average C-index of 0.80, with a log rank p-value << 0.001, confirmed by external testing. Conclusions: Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone, can significantly achieve high predictive performance.

Objectives. Parkinson’s disease (PD) is a complex neurodegenerative disorder, affecting 2%–3% of the elderly population. Montreal Cognitive Assessment (MoCA), a rapid nonmotor screening test, assesses different cognitive dysfunctionality aspects. Early MoCA prediction may facilitate better temporal therapy and disease control. Radiomics features (RF), in addition to clinical features (CF), are indicated to increase clinical diagnoses, etc, bridging between medical imaging procedures and personalized medicine. We investigate the effect of RFs, CFs, and conventional imaging features (CIF) to enhance prediction performance using hybrid machine learning systems (HMLS). Methods. We selected 210 patients with 981 features (CFs, CIFs, and RFs) from the Parkinson’s Progression-Markers-Initiative database. We generated 4 datasets, namely using (i), (ii) year-0 (D1) or year-1 (D2) features, (iii) longitudinal data (D3, putting datasets in years 0 and 1 longitudinally next to each other), and (iv) timeless data (D4, effectively doubling dataset size by listing both datasets from years 0 and 1 separately). First, we directly applied 23 predictor algorithms (PA) to the datasets to predict year-4 MoCA, which PD patients this year have a higher dementia risk. Subsequently, HMLSs, including 14 attribute extraction and 10 feature selection algorithms followed by PAs were employed to enhance prediction performances. 80% of all datapoints were utilized to select the best model based on minimum mean absolute error (MAE) resulting from 5-fold cross-validation. Subsequently, the remaining 20% was used for hold-out testing of the selected models. Results. When applying PAs without ASAs/FEAs to datasets (MoCA outcome range: [11,30]), Adaboost achieved an MAE of 1.74 ± 0.29 on D4 with a hold-out testing performance of 1.71. When employing HMLSs, D4 + Minimum_Redundancy_Maximum_Relevance (MRMR)+K_Nearest_Neighbor Regressor achieved the highest performance of 1.05 ± 0.25 with a hold-out testing performance of 0.57. Conclusion. Our study shows the importance of using larger datasets (timeless), and utilizing optimized HMLSs, for significantly improved prediction of MoCA in PD patients.

Oral cancer remains a major global health issue, with timely diagnosis being essential due to its varied clinical presentation. This study explores how art

Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival largely dependent on early detection. Standard-dose computed tomography (CT) screening, guided by the Lung Imaging Reporting and Data System (Lung-RADS), provides standardized criteria for nodule evaluation. However, interpretation is limited by inter-reader variability and reliance on qualitative descriptors. Radiomics offers quantitative biomarkers but faces challenges of clinical interpretability. In this work, we introduce a radiological-biological dictionary of radiomic features (RFs) that aligns quantitative metrics with Lung-RADS semantic categories, thereby bridging computational and clinical reasoning. We developed a clinically informed dictionary translating Lung-RADS semantic features into RFs through literature curation and expert review. As a proof-of-concept, imaging and clinical data from 977 patients across 12 collections in The Cancer Imaging Archive (TCIA) were analyzed. Following preprocessing and manual segmentation, 110 RFs per nodule were extracted using PyRadiomics in compliance with the Image Biomarker Standardization Initiative (IBSI). A semi-supervised learning (SSL) framework incorporating 499 labeled and 478 unlabeled cases was employed to enhance model generalizability. Seven feature selection techniques and ten interpretable classification models were evaluated. SHapley Additive exPlanations (SHAP) analysis was used to assess correspondence between feature importance and Lung-RADS descriptors. A clinically informed dictionary was developed through literature curation, translating ten Lung-RADS semantic features into corresponding RFs (4-24 RFs per descriptor), and was validated by eight expert reviewers. The best SSL pipeline (ANOVA + support vector machine) achieved a mean validation accuracy of 0.79 ± 0.13. SHAP identifies key radiomic proxies corresponding to Lung-RADS descriptors (e.g., attenuation, margin irregularity, spiculation), which confirmed our dictionary approach. The proposed dictionary provides an interpretable framework linking radiomics and Lung-RADS semantics, advancing explainable artificial intelligence for CT-based lung cancer screening.