Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

Abstract

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample locations are tracked using neuronavigation and genetic aberrations are later quantified through whole-exome and RNA sequencing. The collected specimens from this NCI-funded research study will be primarily used to generate regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status across tumors, within clinically feasible time frames, to identify biopsy and/or treatment targets based on insight from the entire tumor makeup regional histologic and genetic makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma. From October 1, 2017 to October 31, 2022, this study has enrolled 186 patients with 197 surgeries, of which 163 resulted in the successful collection of image-guided biopsy samples. A total of 995 biopsies have been collected of which 962 are image localized, with a mean of 5.90 image-localized samples per surgery.

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients

“…Although we strive to include as much data as possible within model training for the sake of generalizability to the clinic, we may occasionally remove patients from analyses if they do not have the necessary imaging required to generate important model features. Similarly, samples may be removed from analyses if there is a high level of uncertainty or technical error (e.g., coordinates not collected, breakdown in communication in the operating room) during their collection ( 40 ).…”

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

“…A major challenge for such prediction is the lack of large image-localized biopsy datasets (19,20) to train deep learning (DL) models that are well-known to be heavilyparameterized and data-hungry. Creation of large training datasets is limited by various factors such as the invasiveness and high expense of sample acquisition, need of highly-specialized experts to create accurate labels, and difficulty in patient recruitment (19). Moreover, the lack of large datasets has severely limited the number of studies focusing on predicting regional characteristics within each lesion, which are crucial for revealing intratumoral heterogeneity.…”

“…However, precise representations of intratumoral heterogeneity require voxel-wise labels (e.g., image-localized biopsies) that reflect local or regional characteristics of the lesion. A major challenge for such prediction is the lack of large image-localized biopsy datasets (19,20) to train deep learning (DL) models that are well-known to be heavilyparameterized and data-hungry. Creation of large training datasets is limited by various factors such as the invasiveness and high expense of sample acquisition, need of highly-specialized experts to create accurate labels, and difficulty in patient recruitment (19).…”

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

“…However, precise representations of intratumoral heterogeneity require voxel-wise labels (e.g., image-localized biopsies) that reflect local or regional characteristics of the lesion. A major challenge for such prediction is the lack of large image-localized biopsy datasets (19, 20) to train deep learning (DL) models that are well-known to be heavily-parameterized and data-hungry. Creation of large training datasets is limited by various factors such as the invasiveness and high expense of sample acquisition, need of highly-specialized experts to create accurate labels, and difficulty in patient recruitment (19).…”

“…A major challenge for such prediction is the lack of large image-localized biopsy datasets (19, 20) to train deep learning (DL) models that are well-known to be heavily-parameterized and data-hungry. Creation of large training datasets is limited by various factors such as the invasiveness and high expense of sample acquisition, need of highly-specialized experts to create accurate labels, and difficulty in patient recruitment (19). Moreover, the lack of large datasets has severely limited the number of studies focusing on predicting regional characteristics within each lesion, which are crucial for revealing intratumoral heterogeneity.…”