Purpose: Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning. Methods: Fifty-two patients who underwent multi-parametric dual-tracer [¹⁸F]FMC and [⁶⁸Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [⁶⁸Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (M_LH). Furthermore, M_BCR and M_OPR predictive model schemes were built by combining M_LH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [⁶⁸Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses. Results: The area under the receiver operator characteristic curve (AUC) of the M_LH model (0.86) was higher than the AUC of the [⁶⁸Ga]Ga-PSMA-11 SUV_max analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the M_BCR and M_OPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively. Conclusion: Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [¹⁸F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [¹⁸F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUV_max model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [¹⁸F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.

Pituitary adenomas count among the most common intracranial tumors. During pituitary oncogenesis structural, textural, metabolic and molecular changes occur which can be revealed with our integrated ultrahigh-resolution multimodal imaging approach including optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM) on an unprecedented cellular level in a label-free manner. We investigated 5 pituitary gland and 25 adenoma biopsies, including lactotroph, null cell, gonadotroph, somatotroph and mammosomatotroph as well as corticotroph. First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on OCT and MPM images and achieved an accuracy of 88%. Second-level multi-class classification was performed based on molecular analysis of the specimen via LSRM to discriminate pituitary adenomas subtypes with accuracies of up to 99%. Chemical compounds such as lipids, proteins, collagen, DNA and carotenoids and their relation could be identified as relevant biomarkers, and their spatial distribution visualized to provide deeper insight into the chemical properties of pituitary adenomas. Thereby, the aim of the current work was to assess a unique label-free and non-invasive multimodal optical imaging platform for pituitary tissue imaging and to perform a multiparametric morpho-molecular metabolic analysis and classification.

Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.

Ziel/Aim PET/MRI is a promising modality to characterize prostate lesions. To date, the pre-operative diagnosis of prostate cancer is performed by the D’Amico score, utilizing biopsy-based Gleason scoring. We aimed to establish an in vivo D’Amico score for post-operative and biochemical risk prediction built on PET/MRI and machine learning (ML). Methodik/Methods 74 dual-tracer (¹⁸F-FMC+⁶⁸Ga-PSMA^HBED-CC) multi-parametric prostate PET/MRI cases were involved in our study. Gleason-annotated, full-mount histopathological slices guided the PET/MRI lesion delineation (Hermes Hybidref 3D). For each lesion, 56 radiomics features were extracted from PET/MRI as of [1]. Ensemble learning [2] was utilized to build lesion-specific low (=G4) and benign (=G3) risk probability predictors (LH and BM respectively). The summed LH and BM prediction probabilities of the two largest lesions were mapped into +1, +2 and +3 ML risk scores. The in vivo D’Amico score was composed of the ML, the original PSA and the clinical stage risk scores in each patient. 1000-fold Monte Carlo (MC) cross-validation estimated the performance of the in vivo D’Amico score to predict post-operative low vs mid-high risk (IPR) and biochemical recurrence (IBCR) no-yes scores respectively. Ergebnisse/Results The IPR prediction performance was SENS 83 %, SPEC 88 % and ACC 84 % (reference D’Amico SENS 74 %, SPEC 94 % and ACC 80 %). The IBCR performance values were SENS 100 %, SPEC 81 % and ACC 86 % (reference D’Amico SENS 78 %, SPEC 65 % and ACC 69 %). Schlussfolgerungen/Conclusions Our results indicate that the in vivo D’Amico score can accurately predict low vs mid-high post-operative risk as well as biochemical recurrence of prostate patients as the alternative of the biopsy-based D’Amico score.