Patterns of failure for recurrent head and neck squamous cell carcinoma treated with salvage surgery and postoperative IMRT reirradiation.

Purpose/Objectives: The purpose of this study was to evaluate patterns of locoregional recurrence (LRR) after surgical salvage and adjuvant reirradiation with IMRT for recurrent head and neck squamous cell cancer (HNSCC). Materials/Methods: Patterns of LRR for 61 patients treated consecutively between 2003 and 2014 who received post-operative IMRT reirradiation to ≥ 60 Gy for recurrent HNSCC were determined by 2 methods: 1) physician classification via visual comparison of post-radiotherapy imaging to reirradiation plans; and 2) using deformable image registration (DIR). Those without evaluable CT planning image data were excluded. All recurrences were verified by biopsy or radiological progression. Failures were defined as in-field, marginal, or out-of-field. Logistic regression analyses were performed to identify predictors for LRR. Results: A total of 55 patients were eligible for analysis and 23 (42 %) had documented LRR after reirradiation. Location of recurrent disease prior to salvage surgery (lymphatic vs. mucosal) was the most significant predictor of LRR after post-operative reirradiation with salvage rate of 67 % for lymphatic vs. 33 % for mucosal sites (p = 0.037). Physician classification of LRR yielded 14 (61 %) in-field failures, 3 (13 %) marginal failures, and 6 (26 %) out-of-field failures, while DIR yielded 10 (44 %) in-field failures, 4 (17 %) marginal failures, and 9 (39 %) out-of-field failures. Most failures (57 %) occurred within the original site of recurrence or first echelon lymphatic drainage. Of patients who had a free flap placed during salvage surgery, 56 % of failures occurred within 1 cm of the surgical flap. Conclusion: Our study highlights the role of DIR in enhancing the accuracy and consistency of POF analysis. Compared to traditional visual inspection, DIR reduces interobserver variability and provides more nuanced insights into dose-specific and spatial parameters of locoregional recurrences. Additionally, the study identifies the location of the initial recurrence as a key predictor of subsequent locoregional recurrence after salvage surgery and re-IMRT.

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling.

Background: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results: The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Sklodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden.

A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time course of tumour responses to immune checkpoint inhibitors. The model takes into account intrinsic tumour growth rates, the rates of immune activation and of tumour-immune cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug-cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug-cancer sensitivities in individual patients.

Pre-treatment neutrophil/lymphocyte ratio and platelet/lymphocyte ratio are prognostic of progression in early stage classical Hodgkin lymphoma.

To determine whether pre-treatment neutrophil/lymphocyte (NLR) or platelet/lymphocyte ratios (PLR) are predictive for progression in early-stage classical Hodgkin lymphoma (cHL), we derived NLR and PLR values for 338 stage I/II cHL patients and appropriate cut-off point values to define progression. Two-year freedom from progression (FFP) for patients with NLR ≥6·4 was 82·2% vs. 95·7% with NLR <6·4 (P < 0·001). Similarly, 2-year FFP was 84·3% for patients with PLR ≥266·2 vs. 96·1% with PLR <266·2 (P = 0·003). On univariate analysis, both NLR and PLR were significantly associated with worse FFP (P = 0·001). On multivariate analysis, PLR remained a significant, independent prognostic factor (P < 0·001).

Comparison of prostate distortion by inflatable and rigid endorectal MRI coils in permanent prostate brachytherapy imaging.

PURPOSE: To study the deformation of the prostate by a rigid reusable endorectal coil and a balloon-type endorectal coil (BTC) during MRI of the prostate in brachytherapy imaging. METHODS AND MATERIALS: The prostate gland was contoured on 157 MRI scans from 52 prostate cancer patients undergoing brachytherapy. The curvature of the posterior prostate surface deformation was computed as a measure of prostate distortion and compared between scans with a BTC, rigid endorectal coil (REC), or no endorectal coil. For the nine patients who had MRIs with all three endorectal scenarios, a mean prostate deformation vector was also calculated between scenarios using deformable image registration. These measures of prostate distortion were compared with the prostate anterior-to-posterior to left-to-right ratio (AP/LR) on the largest prostate axial slice. RESULTS: Significant differences in prostate curvature were found between scans without an endorectal coil versus a REC versus a BTC (p < 0.001). The mean prostate deformation was 3.9 mm due to the BTC and 2.0 mm for the REC (p = 0.012). The mean AP/LR ratio was 0.62 with a BTC versus 0.76 without a coil or 0.73 with a REC (p < 0.001), but no difference existed between scans with a REC versus no coil (p = 0.7). The AP/LR ratio showed moderate correlation with prostate curvature (r = 0.48), and with mean prostate deformation (r = -0.64 to 0.68). CONCLUSIONS: The REC caused minimal deformation of the prostate compared with a BTC with adequate MR image quality, and calculation of the cross-sectional AP/LR ratio on the largest axial prostate slice can serve as a simple measure of prostate distortion.

Permanent prostate brachytherapy postimplant magnetic resonance imaging dosimetry using positive contrast magnetic resonance imaging markers.

PURPOSE: Permanent prostate brachytherapy dosimetry using computed tomography-magnetic resonance imaging (CT-MRI) fusion combines the anatomic detail of MRI with seed localization on CT but requires multimodality imaging acquisition and fusion. The purpose of this study was to compare the utility of MRI only postimplant dosimetry to standard CT-MRI fusion-based dosimetry. METHODS AND MATERIALS: Twenty-three patients undergoing permanent prostate brachytherapy with use of positive contrast MRI markers were included in this study. Dose calculation to the whole prostate, apex, mid-gland, and base was performed via standard CT-MRI fusion and MRI only dosimetry with prostate delineated on the same T2 MRI sequence. The 3-dimensional (3D) distances between seed positions of these two methods were also evaluated. Wilcoxon-matched-pair signed-rank test compared the D90 and V100 of the prostate and its sectors between methods. RESULTS: The day 0 D90 and V100 for the prostate were 98% versus 94% and 88% versus 86% for CT-MRI fusion and MRI only dosimetry. There were no differences in the D90 or V100 of the whole prostate, mid-gland, or base between dosimetric methods (p > 0.19), but prostate apex D90 was high by 13% with MRI dosimetry (p = 0.034). The average distance between seeds on CT-MRI fusion and MRI alone was 5.5 mm. After additional automated rigid registration of 3D seed positions, the average distance between seeds was 0.3 mm, and the previously observed differences in apex dose between methods was eliminated (p > 0.11). CONCLUSIONS: Permanent prostate brachytherapy dosimetry based only on MRI using positive contrast MRI markers is feasible, accurate, and reduces the uncertainties arising from CT-MRI fusion abating the need for postimplant multimodality imaging.

Permanent prostate brachytherapy pubic arch evaluation with diagnostic magnetic resonance imaging.

PURPOSE: Pubic arch interference (PAI), when it occurs, is often a limiting factor for patients pursuing brachytherapy treatment of prostate cancer. Pre-brachytherapy pubic arch evaluation is often performed by CT or transrectal ultrasound (TRUS), but MRI has increasingly replaced these modalities for prostate cancer evaluation. The purpose of this study was to determine if staging MRI could be used to evaluate PAI and compare it with these other imaging methods. METHODS AND MATERIALS: Forty-one consecutive patients undergoing brachytherapy evaluation had pelvic MRI-, CT-, and TRUS-based brachytherapy simulation. Pubic arch overlap on T2-weighted MRI and CT was determined by contouring the prostate gland on its largest axial slice and superimposing this contour onto the pubic arch bones. The largest degree of overlap of the prostate gland on MRI and CT was used to predict the existence of PAI as determined by TRUS-based simulation. The correlation between prostate contour overlap was also compared between MRI and CT. RESULTS: Nineteen patients (48%) exhibited PAI on TRUS brachytherapy simulation evaluation. The average (±standard error) amount of prostate contour overlap on the pubic arch on CT was 2.9 ± 0.6 mm and on MRI was 2.0 ± 0.6 mm (linear correlation, R, of 0.783, p < 0.001). CT and MRI were equally predictive of PAI on TRUS evaluation (area under the curve = 0.75). CONCLUSION: Pre-brachytherapy pubic arch assessment with diagnostic MRI provides similar predictability of PAI compared with CT, potentially obviating the need for additional pre-brachytherapy CT in the setting of staging MRI.