Benedikt Wiestler’s research while affiliated with Technical University of Munich and other places

Purpose: Due to the highly individualized clinical manifestation of Parkinson's disease (PD), personalized patient care may require domain‐specific assessment of neurological disability. Evidence from magnetic resonance imaging (MRI) studies has proposed that heterogenous clinical manifestation corresponds to heterogeneous cortical disease burden, suggesting customized, high‐resolution assessment of cortical pathology as a candidate biomarker for domain‐specific assessment. Method: Herein, we investigate the potential of the recently proposed Mosaic Approach (MAP), a normative framework for quantifying individual cortical disease burden with respect to a population‐representative cohort, in predicting domain‐specific clinical progression. Using MRI and clinical data from 135 recently diagnosed PD patients from the Parkinson's Progression Markers Initiative, we first defined an extremity‐specific motor score. We then identified cortical regions corresponding to “extremity functions” and restricted MAP, respectively, and contrasted the explanatory power of the extremity‐specific MAP to unrestricted MAP. As control conditions, domain‐related but less specific general motor function and nondomain‐specific cognitive scores were considered. We also tested the predictive power of the restricted MAP in predicting disease progression over 1 and 3 years using support vector machines. The restricted, extremity‐specific MAP yielded higher explanatory power for extremity‐specific motor function at baseline as opposed to the unrestricted, whole‐brain MAP. On the contrary, for general motor function, the unrestricted, whole‐brain MAP yielded higher power. Finding: No associations were found for cognitive function. The extremity‐specific MAP predicted extremity‐specific motor progression over 1 and 3 years above chance level. The MAP framework allows for domain‐specific prediction of customized PD disease progression, which can inform machine learning, thereby contributing to personalized PD patient care.

Purpose In brain tumors, disruption of the blood–brain barrier (BBB) indicates malignancy. Clinical assessment is qualitative; quantitative evaluation is feasible using the K2 leakage parameter from dynamic susceptibility contrast MRI. However, contrast agent–based techniques are limited in patients with renal dysfunction and insensitive to subtle impairments. Assessing water transport times across the BBB (Tex) by multi‐echo arterial spin labeling promises to detect BBB impairments noninvasively and potentially more sensitively. We hypothesized that reduced Tex indicates impaired BBB. Furthermore, we assumed higher sensitivity for Tex than dynamic susceptibility contrast–based K2, because arterial spin labeling uses water as a freely diffusible tracer. Methods We acquired 3T MRI data from 28 patients with intraparenchymal brain tumors (World Health Organization Grade 3 & 4 gliomas [n = 17] or metastases [n = 11]) and 17 age‐matched healthy controls. The protocol included multi‐echo and single‐echo Hadamard‐encoded arterial spin labeling, dynamic susceptibility contrast, and conventional clinical imaging. Tex was calculated using a T2‐dependent multi‐compartment model. Areas of contrast‐enhancing tissue, edema, and normal‐appearing tissue were automatically segmented, and parameter values were compared across volumes of interest and between patients and healthy controls. Results Tex was significantly reduced (−20.3%) in contrast‐enhancing tissue compared with normal‐appearing gray matter and correlated well with |K2| (r = −0.347). Compared with healthy controls, Tex was significantly lower in tumor patients' normal‐appearing gray matter (Tex,tumor = 0.141 ± 0.032 s vs. Tex,HC = 0.172 ± 0.036 s) and normal‐appearing white matter (Tex,tumor = 0.116 ± 0.015 vs. Tex,HC = 0.127 ± 0.017 s), whereas |K2| did not differ significantly. Receiver operating characteristic analysis showed a larger area under the curve for Tex (0.784) than K2 (0.604). Conclusion Tex is sensitive to pathophysiologically impaired BBB. It agrees with contrast agent–based K2 in contrast‐enhancing tissue and indicates sensitivity to subtle leakage.

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications.

Biophysical modeling of brain tumors has emerged as a promising strategy for personalizing radiotherapy planning by estimating the otherwise hidden distribution of tumor cells within the brain. However, many existing state-of-the-art methods are computationally intensive, limiting their widespread translation into clinical practice. In this work, we propose an efficient and direct method that utilizes soft physical constraints to estimate the tumor cell concentration from preoperative MRI of brain tumor patients. Our approach optimizes a 3D tumor concentration field by simultaneously minimizing the difference between the observed MRI and a physically informed loss function. Compared to existing state-of-the-art techniques, our method significantly improves predicting tumor recurrence on two public datasets with a total of 192 patients while maintaining a clinically viable runtime of under one minute - a substantial reduction from the 30 minutes required by the current best approach. Furthermore, we showcase the generalizability of our framework by incorporating additional imaging information and physical constraints, highlighting its potential to translate to various medical diffusion phenomena with imperfect data.

Background Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. Methods We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). Results The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95%CI: 1.43-1.84, p<0.001) and 3.48 (95%CI: 2.94-4.11, p<0.001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. Conclusions Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach for personalized patient management and clinical trial stratification in glioblastoma.

BACKGROUND Glioblastoma is an infiltrative primary brain tumor with poor prognosis despite multimodal therapy. Recurrence is inevitable secondary to tumor cell infiltration in the peritumoral tissues, beyond contrast enhancing margins, which is the target for surgical resection. We hypothesize that a machine learning model constructed from a diverse, inter-institutional dataset can improve accuracy of generated tumor infiltration maps, thus guiding precision targeted therapies. METHODS 731 MRI scans of treatment-naïve glioblastoma patients from 10 institutions were included. All patients had pre-operative multiparametric-MRI (T1, T1Gd, T2, T2-FLAIR, ADC), and underwent complete resection of the enhancing tumor followed by standard-of-care chemoradiotherapy. 42 patients were used as an independent validation set, and 689 were used for training. Of these 239 patients had histopathologically confirmed recurrence with corresponding MRI scans, which were used as ground-truth for evaluating the location of recurrence using a leave-one-site-out (LSO) method. An AI model combining deep learning and SVM was used to develop a predictive model for infiltration. We validated the generalizability of our results in an unseen, multi-institutional data set. RESULTS Our model predicted locations of recurrence with odds ratio (99% CI) 37.6 (37.1-38.1) on the LSO testing set and 24.3 (23.4-25.2) on the validation set, indicating that areas labeled highly infiltrated were over 37 and 24 times more likely to coincide with future recurrence respectively. CONCLUSIONS We demonstrate that AI-based pattern analysis from multiparametric-MRI can predict tumor infiltration in peritumoral regions with high likelihood of recurrence by decrypting the visually imperceptible heterogeneity of peritumoral tissue. Model performance improved from training on a larger/diverse dataset and combining results of multiple AI methods. Independent validation confirmed the model’s ability to generalize to unseen data. We believe this will serve to advance AI-based biomarkers for predicting future recurrence and facilitate development of multi-modal targeted therapies in this era of precision neuro-oncology.

Background Glioblastoma’s infiltrative growth and heterogeneity are influenced by neural, molecular, genetic, and immunological factors, with the precise origin of these tumors remaining elusive. Neurogenic zones might serve as the tumor stem cells’ nest, with tumors in contact with these zones exhibiting worse outcomes and more aggressive growth patterns. This study aimed to determine if these characteristics are reflected in advanced imaging, specifically diffusion and perfusion data. Methods In this monocentric retrospective study, 137 glioblastoma therapy-naive patients (IDH-wildtype, grade 4) with advanced preoperative MRI, including perfusion and diffusion imaging, were analyzed. Tumors and neurogenic zones were automatically segmented. Advanced imaging metrics, including cerebral blood volume (CBV) from perfusion imaging, tissue volume mask (TVM), and free water corrected fractional anisotropy (FA-FWE) from diffusion imaging, were extracted. Results SVZ infiltration positively correlated with CBV, indicating higher perfusion in tumors. Significant CBV differences were noted between high and low SVZ infiltration cases at specific percentiles. Negative correlation was observed with TVM and positive correlation with FA-FWE, suggesting more infiltrative tumor growth. Significant differences in TVM and FA-FWE values were found between high and low SVZ infiltration cases. Discussion Glioblastomas with SVZ infiltration exhibit distinct imaging characteristics, including higher perfusion and lower cell density per voxel, indicating a more infiltrative growth and higher vascularization. Stem cell-like characteristics in SVZ-infiltrating cells could explain the increased infiltration and aggressive behavior. Understanding these imaging and biological correlations could enhance the understanding of glioblastoma evolution.