Interactive Feature Space Explorer© for multi-modal magnetic resonance imaging

Abstract

Wider information content of multi–modal biomedical imaging is advantageous for detection, diagnosis and prognosis of various pathologies. However, the necessity to evaluate a large number images might hinder these advantages and reduce the efficiency. Herein, a new computer aided approach based on the utilization of feature space (FS) with reduced reliance on multiple image evaluations is proposed for research and routine clinical use. The method introduces the physician experience into the discovery process of FS biomarkers for addressing biological complexity, e.g., disease heterogeneity. This, in turn, elucidates relevant biophysical information which would not be available when automated algorithms are utilized. Accordingly, the prototype platform was designed and built for interactively investigating the features and their corresponding anatomic loci in order to identify pathologic FS regions. While the platform might be potentially beneficial in decision support generally and specifically for evaluating outlier cases, it is also potentially suitable for accurate ground truth determination in FS for algorithm development. Initial assessments conducted on two different pathologies from two different institutions provided valuable biophysical perspective. Investigations of the prostate magnetic resonance imaging data resulted in locating a potential aggressiveness biomarker in prostate cancer. Preliminary findings on renal cell carcinoma imaging data demonstrated potential for characterization of disease subtypes in the FS.

Developing a primary tumor and lymph node 18F-FDG PET/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC

“…The heterogeneity of tumors may explain the advantage of evaluating mediastinum LNM by combining the PET-radiomic data of both the primary tumor and the lymph node mechanically. Spatial heterogeneity of tumors in different sites means that the primary tumor may have different molecular phenotypes, imaging features, and metabolism features compared to those of metastatic tumors, and metastatic tumors in different lesions or organs can also have different molecular, imaging, and metabolism features even within the same patient (Chen et al 2019c ; Parker et al 2020 ; Pe'er et al 2021 ; Klughammer et al 2018 ; Schurch et al 2020 ; Ozcan et al 2015 ; Liu et al 2018 ). Therefore, the combination of radiomic features of primary tumor and lymph node may provide more comprehensive information about the lymph nodes, which may be helpful for more accurately diagnosing the N stage of the patient.…”