Mohan Huang

Background: Colorectal cancer remains a major cause of cancer-related mortality worldwide, with poor outcomes in patients with advanced-stage disease. The c-Jun N-terminal kinase (JNK) pathway and hypoxia are important regulators of tumor progression, but their combined prognostic value in colorectal cancer has not been systematically evaluated using machine learning. Methods: Transcriptomic and clinical data from 346 patients with colorectal cancer were divided into training (n = 242) and testing (n = 104) cohorts. Pathway-specific model selection used nested cross-validation with five outer and five inner folds. Within each inner-training fold, minimum redundancy maximum relevance (mRMR) feature selection was repeated, and gene counts of 1–15 were evaluated across nine classifiers, retaining age and sex. An exploratory integrated analysis evaluated combinations selected from the resulting pathway-specific gene pool. An age-and-sex-only model served as a demographic baseline. Results: Results: Training-based selection identified random forest with eight JNK-related genes and histogram-based gradient boosting with 12 hypoxia-related genes. Their testing ROC-AUCs were 0.658 (95% CI: 0.538–0.774) and 0.600 (95% CI: 0.492–0.717), respectively; pooled outer out-of-fold ROC-AUCs were 0.549 and 0.536. The exploratory integrated analysis selected a common 12-gene set. Following parameter exploration and testing-based comparison, K-nearest neighbors achieved a testing ROC-AUC of 0.690 (95% CI: 0.584–0.786), compared with 0.483 (95% CI: 0.368–0.602) for the age-and-sex baseline. Conclusions: Machine learning analysis of JNK- and hypoxia-associated gene signatures suggests potential for predicting 2-year survival in colorectal cancer. The exploratory integrated findings warrant independent external validation, assessment of calibration, and comparison with clinicopathological models before clinical application.

Erysipelothrix rhusiopathiae causes sporadic, often fatal septicaemia in cetaceans; published cases rest almost entirely on cultured isolates, and none has included post-mortem imaging. We investigated a juvenile male short-beaked common dolphin (Delphinus delphis) stranded on Jeju Island, Republic of Korea, using post-mortem computed tomography (PMCT) of the frozen carcass, necropsy, histopathology and tiered molecular testing. Rhomboid skin lesions extended through the skin into the blubber. PMCT documented gravity-dependent pulmonary opacification, fluid in the trachea and bronchi, peritoneal effusion, no decomposition gas and a gastric fishing hook, confirmed at necropsy. Culture was not attempted because the carcass had been stored frozen. Erysipelothrix spp. DNA was detected by broad-range 16S rRNA sequencing in 11 of 12 tissues, and E. rhusiopathiae was identified by species-specific PCR and amplicon sequencing in all 12 tissues, yielding one identical 356-bp sequence. Histopathology showed epidermal coagulative necrosis with septal panniculitis and abundant Gram-positive bacteria throughout the lesion, pulmonary haemorrhage and oedema, interstitial nephritis and reactive lymphadenitis, consistent with fatal septicaemia. The case provides imaging correlates of erysipelas, a culture-independent diagnostic framework for frozen carcasses and a tissue-by-tissue molecular dataset; the relationship of the strain to other Korean cetacean isolates remains to be established genomically.

Background/Objectives: Computed tomography (CT)-based radiomics shows promise for non-small cell lung cancer (NSCLC) prognosis prediction, but model performance varies widely by feature selection and machine learning strategies. Optimal combinations remain unclear. This study aims to systematically compare feature selection methods and machine learning algorithms for 12-month overall survival prediction using CT radiomics in NSCLC patients. Methods: We analyzed 385 patients from The Cancer Imaging Archive (TCIA) NSCLC-Radiomics dataset. Radiomic features from primary tumor volumes were combined with clinical variables. Three feature selection methods—sequential forward selection (SFS), maximum relevance minimum redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO)—were compared across five classifiers: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), and gradient boosting classifier (GBC). Performance was assessed using area under the receiver operating characteristic curve (AUC) and accuracy on independent test sets. Cox regression and Kaplan–Meier analyses evaluated survival risk stratification. Results: Logistic regression showed the most stable classification performance across feature selection strategies (test AUC 0.60–0.65, accuracy 0.72–0.73). The mRMR-LR model achieved highest AUC (0.65); LASSO-LR showed highest accuracy (0.73). For survival analysis, LASSO-based Cox modeling demonstrated superior risk stratification with significant separation between high- and low-risk groups in both training and testing sets (p = 0.0095). Conclusions: Simpler models like logistic regression provide robust performance in CT radiomics, while LASSO excels for survival risk stratification. As we employed single-dataset validation, clinical applicability remains limited because validation was performed within a single public dataset. Nevertheless, the findings provide methodological insights into the selection of feature selection and machine learning strategies for CT radiomics-based prognostic modeling in NSCLC.

Objectives: Glioblastoma multiforme (GBM) is considered the most aggressive primary brain tumor, which often exhibits tumor heterogeneity. Hypoxia is a key aspect of intratumoral heterogeneity that contributes to poor prognosis in GBM. In this study, we aimed to develop machine learning (ML) models using radiomics and clinical features for the prediction of one-year survival for GBM. Methods: Data from 35 patients in the ACRIN 6684 trial, including fluoromisonidazole (FMISO)-positron emission tomography (PET), magnetic resonance (MR) (T1, T2, and fluid-attenuated inversion recovery (FLAIR)) images, and clinical information, were retrieved from The Cancer Imaging Archive (TCIA). Three ML algorithms, namely, support vector machine (SVM), random forest (RF), and linear regression (LR), were utilized to analyze selected features. Receiver-operating characteristic (ROC) curves were utilized to evaluate the predictive performance of the models. Several statistical analyses, namely, the permutation test, the permutation importance of selected features, Fisher’s exact test, and the unpaired t-test, were performed to analyze the models and features. Results: FMISO achieved the best performance in radiomics models, with an area under the curve (AUC) of 0.870. The clinical data model achieved the best performance of all models, with an AUC of 0.921, outperforming the combined all sequential forward selection (SFS) model (AUC: 0.862). Female sex (p = 0.030) and younger age (p = 0.0043) were significantly associated with better prognosis. Conclusions: Our proposed models have the potential to predict the one-year survival of GBM and facilitate personalized therapy. Future studies with a larger sample size are needed to confirm the generalizability of the models.

Radiomics has shown remarkable potential in predicting cancer prognosis by noninvasive and quantitative analysis of tumors through medical imaging. This review summarizes recent advances in the use of radiomics across various cancer types and imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and interventional radiology. Innovative sampling methods, including deep learning-based segmentation, multiregional analysis, and adaptive region of interest (ROI) methods, have contributed to improved model performance. The review examines various feature selection approaches, including least absolute shrinkage and selection operator (LASSO), minimum redundancy maximum relevance (mRMR), and ensemble methods, highlighting their roles in enhancing model robustness. The integration of radiomics with multi-omics data has further boosted predictive accuracy and enriched biological interpretability. Despite these advancements, challenges remain in terms of reproducibility, workflow standardization, clinical validation and acceptance. Future research should prioritize multicenter collaborations, methodological coordination, and clinical translation to fully unlock the prognostic potential of radiomics in oncology.

Tumor hypoxia involves limited oxygen supply within the tumor microenvironment and is closely associated with aggressiveness, metastasis, and resistance to common cancer treatment modalities such as chemotherapy and radiotherapy. Traditional methodologies for hypoxia assessment, such as the use of invasive probes and clinical biomarkers, are generally not very suitable for routine clinical applications. Radiomics provides a non-invasive approach to hypoxia assessment by extracting quantitative features from medical images. Thus, radiomics is important in diagnosis and the formulation of a treatment strategy for tumor hypoxia. This article discusses the various imaging techniques used for the assessment of tumor hypoxia including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). It introduces the use of radiomics with machine learning and deep learning for extracting quantitative features, along with its possible clinical use in hypoxic tumors. This article further summarizes the key challenges hindering the clinical translation of radiomics, including the lack of imaging standardization and the limited availability of hypoxia-labeled datasets. It also highlights the potential of integrating radiomics with multi-omics to enhance hypoxia visualization and guide personalized cancer treatment.