M. Kamal

Quantitative pretreatment CT volumetry: Association with oncologic outcomes in patients with T4a squamous carcinoma of the larynx

The purpose of this study was to determine the impact of CT‐determined pretreatment primary tumor volume on survival and disease control in T4a laryngeal squamous cell carcinoma (SCC).

Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.

Patient reported dry mouth: Instrument comparison and model performance for correlation with quality of life in head and neck cancer survivors

PURPOSE To identify a clinically meaningful cut-point for the single item dry mouth question of the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN). METHODS Head and neck cancer survivors who had received radiation therapy (RT) completed the MDASI-HN, the University of Michigan Hospital Xerostomia Questionnaire (XQ), and the health visual analog scale (VAS) of the EuroQol Five Dimension Questionnaire (EQ-5D). The Bayesian information criteria (BIC) were used to test the prediction power of each tool for EQ-5D VAS. The modified Breiman recursive partitioning analysis (RPA) was used to identify a cut point of the MDASI-HN dry mouth score (MDASI-HN-DM) with EQ-5D VAS, using a ROC-based approach; regression analysis was used to confirm the threshold effect size. RESULTS Two-hundred seven respondents formed the cohort. Median follow-up from the end of RT to questionnaire completion was 88 months. The single item MDASI-HN-DM score showed a linear relationship with the XQ composite score (ρ = 0.80, p < 0.001). The MDASI-HN-DM displayed improved model performance for association with EQ-5D VAS as compared to XQ (BIC of 1803.7 vs. 2016.9, respectively). RPA showed that an MDASI-HN-DM score of ≥6 correlated with EQ-5D VAS decline (LogWorth 5.5). CONCLUSION The single item MDASI-HN-DM correlated with the multi-item XQ and performed favorably in the prediction of QOL. A MDASI-HN-DM cut point of ≥6 correlated with decline in QOL.

Dose-volume correlates of mandibular osteoradionecrosis in Oropharynx cancer patients receiving intensity-modulated radiotherapy: Results from a case-matched comparison

PURPOSE To determine dosimetric parameters associated with osteoradionecrosis (ORN) in oropharyngeal cancer (OPC) patients in the IMRT era. MATERIAL AND METHODS Subsequent to institutional review board approval, we identified ORN in OPC patients treated with IMRT from 2002 to 2013. 1:2 case-control matching was implemented. Mandibular dose-volume histograms (DVH) were extracted. Dosimetric parameters were compared using non-parametric stats. Recursive partitioning analysis (RPA) was done to identify DVH correlates of ORN. RESULTS 68 ORN cases and 131 controls were matched. Median follow-up was 41months and median time to development of ORN was 16months. Mandibular mean dose was significantly higher in the ORN cohort (48.1 vs 43.6Gy, p<0.0001). However, the maximum dose was not statistically different. DVH bins from V35 to V73 were all significantly higher in the ORN cohort compared with controls (p<0.0006). Two DVH parameters were identified in RPA analysis, V43 and V58. The majority (81%) of ORN cases were observed with both V44≥42% and V58≥25%. CONCLUSIONS Our data demonstrate that a wide range of DVH parameters in the intermediate and high beam path were all significantly higher in ORN patients. Mandibular V44<42% and V58<25% represent reasonable DVH constraints for IMRT plan acceptability, when tumor coverage is not compromised.

Prospective in silico study of the feasibility and dosimetric advantages of MRI-guided dose adaptation for human papillomavirus positive oropharyngeal cancer patients compared with standard IMRT

Highlights • The average dose to 95% of initial PTV volume was 70.7 Gy for standard plans vs. 58.5 Gy for adaptive plans.• MRI-guided adaptive approach resulted in decrease dose to normal tissue compared with standard plans.• NTCP of post-treatment dysphagia, feeding tube, and hypothyroidism were reduced using the adaptive approach.

Radiotherapy dose–volume parameters predict videofluoroscopy-detected dysphagia per DIGEST after IMRT for oropharyngeal cancer: Results of a prospective registry

PURPOSE Our primary aim was to prospectively validate retrospective dose-response models of chronic radiation-associated dysphagia (RAD) after intensity modulated radiotherapy (IMRT) for oropharyngeal cancer (OPC). The secondary aim was to validate a grade ≥2 cut-point of the published videofluoroscopic dysphagia severity (Dynamic Imaging Grade for Swallowing Toxicity, DIGEST) as radiation dose-dependent. MATERIAL AND METHODS Ninety-seven patients enrolled on an IRB-approved prospective registry protocol with stage I-IV OPC underwent pre- and 3-6 month post-RT videofluoroscopy. Dose-volume histograms (DVH) for swallowing regions of interest (ROI) were calculated. Dysphagia severity was graded per DIGEST criteria (dichotomized with grade ≥2 as moderate/severe RAD). Recursive partitioning analysis (RPA) and Bayesian Information Criteria (BIC) were used to identify dose-volume effects associated with moderate/severe RAD. RESULTS 31% developed moderate/severe RAD (i.e. DIGEST grade ≥2) at 3-6 months after RT. RPA found DVH-derived dosimetric parameters of geniohyoid/mylohyoid (GHM), superior pharyngeal constrictor (SPC), and supraglottic region were associated with DIGEST grade ≥2 RAD. V61 ≥ 18.57% of GHM demonstrated optimal model performance for prediction of DIGEST grade ≥2. CONCLUSION The findings from this prospective longitudinal registry validate prior observations that dose to submental musculature predicts for increased burden of dysphagia after oropharyngeal IMRT. Findings also support dichotomization of DIGEST grade ≥2 as a dose-dependent split for use as an endpoint in trials or predictive dose-response analysis of videofluoroscopy results.

Auto-delineation of oropharyngeal clinical target volumes using three-dimensional convolutional neural networks

Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor spread is necessary. This paper proposes a solution to auto-segment these volumes in oropharyngeal cancer patients using a two-channel 3D U-Net architecture. The first channel feeds the network with the patients CT image providing anatomical context, whereas the second channel provides the network with tumor location and morphological information. Radiation therapy simulation computer tomography scans and their corresponding manually delineated CTV and gross tumor volume (GTV) delineations from 285 oropharyngeal patients previously treated at MD Anderson Cancer Center were used in this study. CTV and GTV delineations underwent rigorous group peer-review prior to the start of treatment delivery. The convolutional networks parameters were fine-tuned using a training set of 210 patients using 3-fold cross-validation. During hyper-parameter selection, we use a score based on the overlap (dice similarity coefficient (DSC)) and missed volumes (false negative dice (FND)) to minimize any possible under-treatment. Three auto-delineated models were created to estimate tight, moderate, and wide CTV margin delineations. Predictions on our test set (75 patients) resulted in auto-delineations with high overlap and close surface distance agreement (DSC  >  0.75 on 96% of cases for tight and moderate auto-delineation models and 97% of cases having mean surface distance  ⩽  5.0 mm) to the ground-truth. We found that applying a 5 mm uniform margin expansion to the auto-delineated CTVs would cover at least 90% of the physician CTV volumes for a large majority of patients; however, determination of appropriate margin expansions for auto-delineated CTVs merits further investigation.

Three-dimensional imaging assessment of anatomic invasion and volumetric considerations for chemo/radiotherapy-based laryngeal preservation in T3 larynx cancer

OBJECTIVES To investigate the impact of 3-Diminsional (3D) tumor volume (TV) and extent of involvement of primary tumor on treatment outcomes in a large uniform cohort of T3 laryngeal carcinoma patients treated with nonsurgical laryngeal preservation strategies. MATERIALS AND METHODS The pretreatment contrast-enhanced computed tomography images of 90 patients with T3 laryngeal carcinoma were reviewed. Primary gross tumor volume (GTVp) was delineated to calculate the 3D TV and define the extent of invasion. Cartilage and soft tissue involvement was coded. The extent of invasion was dichotomized into non/limited invasion versus multiple invasion extension (MIE), and was subsequently correlated with survival outcomes. RESULTS The median TV was 6.6 cm3. Sixty-five patients had non/limited invasion, and 25 had MIE. Median follow-up for surviving patients was 52 months. The 5-year local control and overall survival rates for the whole cohort were 88% and 68%, respectively. There was no correlation between TV and survival outcomes. However, patients with non/limited invasion had better 5-year local control (LC) than those with MIE (95% vs 72%, p = .009) but did not have a significantly higher rate of overall survival (OS) (74% vs 67%, p = .327). In multivariate correlates of LC, MIE maintained statistical significance whereas baseline airway status showed a statistically significance trend with poor LC (p = .0087 and 0.06, respectively). Baseline good performance status was an independent predictor of improved OS (p = .03) in multivariate analysis. CONCLUSION The extent of primary tumor invasion is an independent prognostic factor of LC of the disease after definitive radiotherapy in T3 larynx cancer.

Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA

Purpose Recent data suggest that imaging radiomics features for a tumor could predict important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. For Head and Neck Squamous Cell Carcinoma (HNSCC), we perform a comprehensive study to discover the imaging-genomics associations and explore the potential of predicting tumor genomic alternations using radiomic features. Methods Our retrospective study integrates whole-genome multi-omics data from The Cancer Genome Atlas (TCGA) with matched computed tomography imaging data from The Cancer Imaging Archive (TCIA) for the same set of 126 HNSCC patients. Linear regression analysis and gene set enrichment analysis are used to identify statistically significant associations between radiomic imaging features and genomic features. Random forest classifier is used to predict two key HNSCC molecular biomarkers, the status of human papilloma virus (HPV) and disruptive TP53 mutation, based on radiomic features. Results Wide-spread and statistically significant associations are discovered between genomic features (including miRNA expressions, protein expressions, somatic mutations, and transcriptional activities, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of HPV and TP53 mutation status using radiomic features achieves an area under the receiver operating characteristics curve (AUC) of 0.71 and 0.641, respectively. Conclusion Our analysis suggests that radiomic features are associated with genomic characteristics in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.