Comparative Performance of Random Forest and Support Vector Machine Classifiers for Detection of Colorectal Lesions in CT Colonography

Machine Learning Approaches for Neuroblastoma Risk Prediction and Stratification

Machine learning (ML) holds great promise in advancing risk prediction and stratification for neuroblastoma, a highly heterogeneous pediatric cancer. By utilizing large-scale biological and clinical data, ML models can detect complex patterns that traditional approaches often overlook, enabling more personalized treatments and better patient outcomes. Various ML techniques, such as support vector machines, random forests, and deep learning, have shown superior performance in predicting survival, relapse, and treatment responses in neuroblastoma patients compared to conventional methods. However, challenges like limited data size, model interpretability, data variability, and difficulties in clinical integration hinder broader adoption. Additionally, ethical concerns related to bias and privacy must be addressed. Future work should focus on improving data quality, enhancing model transparency, and conducting thorough clinical validation. With these advancements, ML has the potential to revolutionize neuroblastoma care by refining early diagnosis, risk assessment, and therapeutic decision-making.

Volumetric detection of colorectal lesions for noncathartic dual-energy computed tomographic colonography

Noncathartic computed tomographic colonography (CTC) could significantly increase patient adherence to colorectal screening guidelines. However, radiologists find the interpretation of noncathartic CTC images challenging. We developed a fully automated computer-aided detection (CAD) scheme for assisting radiologists with noncathartic CTC. A volumetric method is used to detect lesions within a thick target region encompassing the colonic wall. Dual-energy CTC (DE-CTC) is used to provide more detailed information about the colon than what is possible with conventional CTC. False-positive detections are reduced by use of a random-forest classifier. The effect of the thickness of the target region on detection performance was assessed by use of 22 clinical noncathartic DE-CTC studies including 27 lesions ≥6 mm. The results indicate that the thickness parameter can have significant effect on detection accuracy. Leave-one-patient-out evaluation indicated that the proposed CAD scheme detects colorectal lesions at high accuracy in noncathartic CTC.

Computer-Aided Detection of Colorectal Lesions with Super-Resolution CT Colonography: Pilot Evaluation

Reliable computer-aided detection (CADe) of small polyps and flat lesions is limited by the relatively low image resolution of computed tomographic colonography (CTC). We developed a sinogram-based super-resolution (SR) method to enhance the images of lesion candidates detected by CADe. First, CADe is used to detect lesion candidates at high sensitivity from conventional CTC images. Next, the signal patterns of the lesion candidates are enhanced in sinogram domain by use of non-uniform compressive sampling and iterative reconstruction to produce SR images of the lesion candidates. For pilot evaluation, an anthropomorphic phantom including simulated lesions was filled partially with fecal tagging and scanned by use of a CT scanner. A fully automated CADe scheme was used to detect lesion candidates in the images reconstructed at conventional 0.61-mm and at 0.10-mm SR image resolution. The proof-of-concept results indicate that the SR method has potential to reduce the number of FP CADe detections below that obtainable with the conventional CTC imaging technology.