This revised project is designed to investigate a new safe, non-radioactive HP 13C mpMRI exam approach for creating a metabolic imaging solution for the unmet clinical need of detecting aggressive prostate cancer in patients prior to enrollment or on “Active Surveillance” with correlations to “gold standard” MR/US fusionguided biopsy findings, and other clinical measures including current imaging parameters. This project was designed to fit the goals of PAR-16-089 Imaging and Biomarkers for Early detection of Aggressive Cancer that states: “The specific objective of this FOA is to stimulate and support cancer imaging and biomarker research to develop, optimize, and clinically validate novel methods” to address the ““unmet clinical need to more accurately identify early-stage aggressive cancers and distinguish lesions that are life threatening from those that are not”. In response to the prior critique, we have significantly modified this study application following the reviewers' suggestions and addressing their concerns. The clinicians on our multidisciplinary team designed this revised study based on their unmet need to identify which untreated prostate cancer patients (thousands per year) have only low risk disease and can be managed by Active Surveillance (AS) and which have aggressive cancer, missed on biopsy, but still organ-confined and should be treated. Molecular imaging with FDG- or PSMA-PET is used for detecting metastatic disease, but high uptake in the bladder and normal prostatic tissues hinders the critical detection of high-grade cancer within the prostate. Based on strong preliminary preclinical & patient data showing a significant correlation of elevated HP 13C-pyruvate to 13C-lactate conversion in high grade prostate cancer, our clinical team created this study design testing improved, safe, lower-cost HP 13C MR molecular imaging techniques as a 5min addition to a standard-of-care mpMRI exam with correlations to subsequent MR/US fusion-guided biopsy findings; “goldstandard” for this patient population. We aim to study patients (N=110) referred for mpMRI either to rule out missed aggressive disease at biopsy diagnosis prior to deciding on AS or as a consequence of a rising PSA while on AS. Also a subset (N=44) of these patients that enter AS, will be followed yearly with HP+mpMRI exams followed by MR/US fusion-guided biopsies in order to determine if a significant increase in kPL of any intra-prostatic lesion is an early predictor of disease progression (Gleason score upgrading). The success of this project could have exceptional clinical impact including: 1) Increased confidence that those entering AS do not have aggressive prostate cancer; 2) Early detection of aggressive cancer in AS patients; and 3) Improved guidance of biopsy and subsequent treatment of metabolically aggressive organ-confined cancer.

Lung cancer is a leading cause of death in the United States. The National Lung Screening Trial (NLST) showed that more lung cancers can be detected at an early stage with low dose CT screening. However, over-diagnosis of indolent lung cancer and benign nodules is one of the major limitations of screening, resulting in unnecessary treatment, biopsy, follow-up, increased radiation exposure, patient anxiety, and cost. Due to a lack of in-depth knowledge of the correlation of structural image features and histologic findings of lung nodules and the absence of validated diagnostic biomarkers for accurate disease categorization, the current diagnosis and management of the screen-detected nodules remains challenging. The goal of this proposed project is to develop a decision support system (DSS) based on quantitative histopathology correlated CT descriptor (q-PCD) of pulmonary nodules using advanced computer vision and machine learning techniques to characterize the histopathologic features of nodules and analyze their correlations with CT image features for improvement of early detection of lung cancer. We hypothesize that the proposed q-PCD analysis will have strong association with histopathologic characterization, and therefore will be a more effective biomarker for differentiation of invasive, pre-invasive, and benign nodules than conventional image-based features or radiologists' visual judgement. Accurate characterization of the nodule types will assist radiologists in making decision for management of the detected nodules; e.g., enabling early detection and treatment of invasive lung cancer, safe surveillance or replacing lobectomy with limited sublobar resection for pre-invasive lung cancer, and sparing biopsy of benign nodules, thereby reducing morbidity and costs in lung cancer screening programs. Our major specific aims are to 1) collect a large database of LDCT screening cases from NLST project and our institute to develop automated image analysis methods, 2) to develop a new DSS based on quantitative pathologic correlated CT descriptors (q-PCD) of lung nodules, 3) validate the effectiveness of DSS in lung cancer diagnosis. To achieve these aims, we will collect a large data set from the National Lung Screening Trial (NLST) and our institute. The collected database will include the baseline and follow up scans, pathology data, demographic information and other information provided by NLST. We will develop automated segmentation methods to extract the volumes of the solid and sub-solid components of detected lung nodules, develop quantitative methods to characterize the radiologic and pathologic features of lung nodules as well as the surrounding lung parenchyma, develop a novel radiopathomics strategy to correlate pathomics with radiomics, and to identify new imaging biomarkers. We will develop a clinically-translatable DSS with a joint biomarker combining both image and patient information, and evaluate its performance in lung cancer diagnosis, including its effectiveness in baseline screening CT exams and in follow up exams.