Quantitative Imaging Analysis of Non-Small Cell Lung Cancer
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CitationAgrawal, Vishesh. 2016. Quantitative Imaging Analysis of Non-Small Cell Lung Cancer. Doctoral dissertation, Harvard Medical School.
AbstractQuantitative imaging is a rapidly growing area of interest within the field of bioinformatics and biomarker discovery. Due to the routine nature of medical imaging, there is an abundance of high-quality imaging linked to clinical and genetic data. This data is particularly relevant for cancer patients who receive routine CT imaging for staging and treatment purposes. However, current analysis of tumor imaging is generally limited to two-dimensional diameter measurements and assessment of anatomic disease spread. This conventional tumor-node-metastasis (TNM) staging system stratifies patients to treatment protocols including decisions regarding adjuvant therapy. Recently there have been several studies suggesting that these images contain additional unique information regarding tumor phenotype that can further aid clinical decision-making.
In this study I aimed to develop the predictive capability of medical imaging. I employed the principles of quantitative imaging and applied them to patients with non-small cell lung cancer (NSCLC). Quantitative imaging, also termed radiomics, seeks to extract thousands of imaging data points related to tumor shape, size and texture. These data points can potentially be consolidated to develop a tumor signature in the same way that a tumor might contain a genetic signature corresponding to mutational burden. To accomplish this I applied radiomics analyses to patients with early and late stage NSCLC and tested these for correlation with both histopathological data as well as clinical outcomes.
Patients with both early and late stage NSCLC were assessed. For locally advanced NSCLC (LA-NSCLC), I analyzed patients treated with preoperative chemoradiation followed by surgical resection. To assess early stage NSCLC, I analyzed patients treated with stereotactic body radiation therapy (SBRT). Quantitative imaging features were extracted from CT imaging obtained prior to chemoradiation and post-chemoradiation prior to surgical resection. For patients who underwent SBRT, quantitative features were extracted from cone-beam CTs (CBCT) at multiple time points during therapy. Univariate and multivariate logistic regression were used to determine association with pathologic response. Concordance-index and Kaplan-Meier analyses were applied to time dependent endpoints of overall survival, locoregional recurrence-free and distant metastasis.
In this study, 127 LA-NSCLC patients were identified and treated with preoperative chemoradiation and surgical resection. 99 SBRT patients were identified in a separate aim of this study. Reduction of CT-defined tumor volume (OR 1.06 [1.02-1.09], p=0.002) as continuous variables per percentage point was associated with pathologic complete response (pCR) and locoregional recurrence (LRR). Conventional response assessment determined by diameter (p=0.213) was not associated with pCR or any survival endpoints. Seven texture features on pre-treatment tumor imaging were associated with worse pathologic outcome (AUC 0.61-0.66). Quantitative assessment of lymph node burden demonstrated that pre-treatment and post-treatment volumes are significantly associated with both OS and LRR (CI 0.62-0.72). Textural analyses of these lymph nodes further identified 3 unique pre-treatment and 7 unique post-treatment features significantly associated with either LRR, DM or OS. Finally early volume change showed associated with overall survival in CBCT scans of early NSCLC.
Quantitative assessment of NSCLC is thus strongly associated with pathologic response and survival endpoints. In contrast, conventional imaging response assessment was not predictive of pathologic response or survival endpoints. This study demonstrates the novel application of radiomics to lymph node texture, CBCT volume and patients undergoing neoadjuvant therapy for NSCLC. These examples highlight the potential within the rapidly growing field of quantitative imaging to better describe tumor phenotype. These results provide evidence to the growing radioimics literature that there is significant association between imaging, pathology and clinical outcomes. Further exploration will allow for more complete models describing tumor imaging phoentype with clinical outcomes.
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