Dataset Information

Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer.

ABSTRACT: The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on 18F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4-13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7-12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.

SUBMITTER: Wu J

PROVIDER: S-EPMC7067523 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer.

Wu Jia J Gensheimer Michael F MF Zhang Nasha N Guo Meiying M Liang Rachel R Zhang Carrie C Fischbein Nancy N Pollom Erqi L EL Beadle Beth B Le Quynh-Thu QT Li Ruijiang R

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20190816 3

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. <b>Methods:</b> We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiothera ...[more]

PMID: 31420498

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Project description:Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

Project description:To measure the early therapeutic response to a novel apoptosis-inducing antibody, TRA-8, by using diffusion-weighted magnetic resonance (MR) imaging in a mouse breast cancer model.Animal experiments had institutional animal care and use committee approval. Four groups of nude mice bearing luciferase-positive breast tumors (four to five mice with eight to 10 tumors per group) were injected intravenously with 0 mg (group 1), 0.025 mg (group 2), 0.100 mg (group 3), or 0.200 mg (group 4) of TRA-8 on days 0 and 3. Diffusion-weighted imaging, anatomic MR imaging, and bioluminescence imaging were performed on days 0, 3, and 6 before dosing. Averaged apparent diffusion coefficients (ADCs) for both whole tumor volume and a 1-mm peripheral tumor shell were calculated and were compared with tumor volume and living tumor cell changes. After imaging at day 6, proliferating and apoptotic cell densities were measured with Ki67 and terminal deoxynucleotidyl transferase mediated dUTP nick end labeling, or TUNEL, staining, respectively, and were compared with cleaved caspase-3 density.The ADC increase at day 3 was dependent on TRA-8 dose level, averaging 6% +/- 3 (standard error of mean), 19% +/- 4, 14% +/- 4, and 34% +/- 7 in the whole tumor volume and 1% +/- 2, 9% +/- 5, 13% +/- 5, and 30% +/- 8 in the outer 1-mm tumor shell only for groups 1, 2, 3, and 4, respectively. The ADC increase in group 4 was significantly higher (P = .0008 and P = .0189 for whole tumor volume and peripheral region, respectively) than that in group 1 on day 3, whereas tumor size did not significantly differ. At day 3, the dose-dependent ADC increases were linearly proportional to apoptotic cell and cleaved caspase-3 densities and were inversely proportional to the density of cells showing Ki67 expression.Diffusion-weighted imaging enabled measurement of early breast tumor response to TRA-8 treatment, prior to detectable tumor shrinkage, providing an effective mechanism to noninvasively monitor TRA-8 efficacy.http://radiology.rsnajnls.org/cgi/content/full/248/3/844/DC1.

Dataset Information

Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer.

Publications

Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer.

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