Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma.

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

[Classification of malignant melanomas of skin (author's transl)]. / Die Typisierung maligner melanome der Haut.

Classification of 241 malignant melanomas was made by computer-aided analysis of 14 clinical and 17 histological variables accomplished by clinical diapositives and renewed histological sections. Marked type-related parameters concerned, among others, tumor location, sex and age of patient as well as histological variables of epidermal thickness and surface, intracorneal pigment, sort of melanocytic dysplasia, follicular involvement, dermal elastosis, increased peritumoral vascularity. Tumors demonstrating histologically combined features of SSM and LMM and also spindle celled verrucous melanomas had all to be classified as SSM. Adequate typification of malignant skin melanomas solely by histology provides representative sectioning guided by the histopathologist and then is possible in nearly every case.