Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

BACKGROUND: Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. Therapeutic targeting of miR-155 through its antagonist, anti-miR-155, has proven challenging due to its dual molecular effects. METHODS: We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. RESULTS: Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimens to prevent antagonistic effects. CONCLUSIONS: This work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

Improving the clinical meaning of surrogate endpoints: An empirical assessment of clinical progression in phase III oncology trials.

Disease progression in clinical trials is commonly defined by radiologic measures. However, clinical progression may be more meaningful to patients, may occur even when radiologic criteria for progression are not met, and often requires a change in therapy in clinical practice. The objective of this study was to determine the utilization of clinical progression criteria within progression-based trial endpoints among phase III trials testing systemic therapies for metastatic solid tumors. The primary manuscripts and protocols of phase III trials were reviewed for whether clinical events, such as refractory pain, tumor bleeding, or neurologic compromise, could constitute a progression event. Univariable logistic regression computed odds ratios (OR) and 95% CI for associations between trial-level covariates and clinical progression. A total of 216 trials enrolling 148,190 patients were included, with publication dates from 2006 through 2020. A major change in clinical status was included in the progression criteria of 13% of trials (n = 27), most commonly as a secondary endpoint (n = 22). Only 59% of trials (n = 16) reported distinct clinical progression outcomes that constituted the composite surrogate endpoint. Compared with other disease sites, genitourinary trials were more likely to include clinical progression definitions (16/33 [48%] vs. 11/183 [6%]; OR, 14.72; 95% CI, 5.99 to 37.84; p < .0001). While major tumor-related clinical events were seldom considered as disease progression events, increased attention to clinical progression may improve the meaningfulness and clinical applicability of surrogate endpoints for patients with metastatic solid tumors.

Addition of Metastasis-Directed Therapy to Systemic Therapy for Oligometastatic Pancreatic Ductal Adenocarcinoma (EXTEND): A Multicenter, Randomized Phase II Trial.

PURPOSE: The EXTEND trial tested the hypothesis that adding comprehensive metastasis-directed therapy (MDT) to chemotherapy would improve progression-free survival (PFS) over chemotherapy alone among patients with oligometastatic pancreatic ductal adenocarcinoma (PDAC). METHODS: EXTEND (ClinicalTrials.gov identifier: NCT03599765) is a multicenter, phase II basket trial randomly assigning patients with ≤five metastases 1:1 to MDT plus systemic therapy versus systemic therapy. Disease progression was defined by radiologic criteria (RECIST v1.1), clinical progression, or death. The primary end point was PFS in the per-protocol population, evaluated after all patients achieved at least 6 months of follow-up. Exploratory end points included systemic immune response measures. RESULTS: Between March 19, 2019, and February 13, 2023, 41 patients were randomly assigned and 40 were eligible for the primary analysis of PFS (19 patients in the MDT arm; 21 patients in the control arm). At a median follow-up time of 17 months, the median PFS time was 10.3 months (95% CI, 4.6 to 14.0) in the MDT arm versus 2.5 months (95% CI, 1.7 to 5.1) in the control arm. PFS was significantly improved by the addition of MDT to systemic therapy (P = .030 for stratified log-rank test) with a hazard ratio of 0.43 (95% CI, 0.20 to 0.94). No grade ≥3 or greater adverse events related to MDT were observed. Systemic immune activation events were associated with MDT and correlated with improved PFS. CONCLUSION: This study supports the addition of MDT to systemic therapy for patients with oligometastatic PDAC. Induction of systemic immunity is a possible mechanism of benefit. These results warrant confirmatory trials to refine treatment strategy and provide external validation.

Clinical Validation of Mathematically Derived Early Tumor Dynamics for Solid Tumors in Response to Durvalumab.

PURPOSE: Early prediction of response to immunotherapy may help guide patient management by identifying resistance to treatment and allowing adaptation of therapies. This analysis evaluated a mathematical model of response to immunotherapy that provides patient-specific prediction of outcome using the initial change in tumor size/burden from baseline to the first follow-up visit on standard imaging scans. METHODS: We applied the model to 600 patients with advanced solid tumors who received durvalumab in Study 1108, a phase I/II trial, and compared outcome prediction performance versus size-based criteria with RECIST version 1.1 best overall response (BOR), baseline circulating tumor (ct)DNA level, and other clinical/pathologic predictors of immunotherapy response. RESULTS: In multiple solid tumors, the mathematical parameter representing net tumor growth rate at the first on-treatment computed tomography (CT) scan assessed around 6 weeks after starting durvalumab (α1) had a concordance index to predict overall survival (OS) of 0.66-0.77 on multivariate analyses. This measurement of early tumor dynamics significantly improved multivariate OS models that included standard RECIST v1.1 criteria, baseline ctDNA levels, and other clinical/pathologic factors in predicting OS. Furthermore, α1 was assessed consistently at the first on-treatment CT scan, whereas all traditional RECIST BOR groups were confirmed only after this time. CONCLUSION: These results support further exploring α1 as an integral biomarker of response to immunotherapy. This biomarker may be predictive of further benefit and can be assessed before RECIST response groups can be assigned, potentially providing an opportunity to personalize oncologic management.

Development and clinical implementation of a digital workflow utilizing 3D-printed oral stents for patients with head and neck cancer receiving radiotherapy.

OBJECTIVES: We describe the development of 3D-printed stents using our digital workflow and their effects on patients enrolled in the lead-in phase of a multi-center, randomized Phase-II trial. MATERIALS AND METHODS: Digital dental models were created for patients using intraoral scanning. Digital processes were implemented to develop the mouth-opening, tongue-depressing, and tongue-lateralizing stents using stereolithography. Time spent and material 3D-printing costs were measured. Physicians assessed mucositis using the Oral Mucositis Assessment Scale (OMAS) and collected MD Anderson Symptom Inventory (MDASI) reports and adverse events (AEs) from patients at various time points (TPs). OMAS and MDASI results were evaluated using paired t-test analysis. RESULTS: 18 patients enrolled into the lead-in phase across 6 independent clinical sites in the USA. 15 patients received stents (average design and fabrication time, 8 h; average material 3D-printing cost, 11 USD). 10 eligible patients with complete OMAS and MDASI reports across all TPs were assessed. OMAS increased significantly from baseline to week 3 of treatment (mean difference = 0.34; 95 % CI, 0.09-0.60; p = 0.01). MDASI increased significantly from baseline to week 3 of treatment (mean difference = 1.02; 95 % CI, 0.40-1.70; p = 0.005), and week 3 of treatment to end of treatment (mean difference = 1.90; 95 % CI, 0.90-2.92; p = 0.002). AEs (grades 1-3) were reported by patients across TPs. Mucositis and radiation dermatitis were primarily attributed to chemoradiation. CONCLUSIONS: 3D-printed stents were successfully fabricated and well tolerated by patients. As patients enroll in the randomized phase of this trial, data herein will establish a baseline for comparative analysis.

EUS-guided hydrogel injection to separate pancreatic head carcinoma from duodenum for enhanced radiotherapy: Multi-site feasibility study.

Background and study aims The proximity of a pancreas head tumor to the duodenum often limits delivery of an ablative dose of radiation therapy. This study evaluated the feasibility and safety of using an injectable polyethylene glycol (PEG) hydrogel between the head of the pancreas and duodenum. Patients and methods In a multi-site feasibility cohort study of patients with localized pancreatic cancer, PEG hydrogel was injected under endoscopic ultrasound guidance to temporarily position the duodenum away from the pancreas. Procedure characteristics were recorded, including hydrogel volume and space created. Patients were monitored for adverse events (AEs) and radiotherapy toxicity. Results In all six intent-to-treat patients (four with borderline resectable, two with locally advanced disease), the ability to place and visualize PEG hydrogel and create space between the duodenum and the head of the pancreas was successful. There were no procedure-related AEs resulting in radiotherapy delay. There were no device-related AEs and no reports of pancreatitis. Conclusions PEG hydrogel was successfully placed, created space between the duodenum and the head of the pancreas, and was not associated with major toxicity. Enhancing radiotherapy for pancreatic cancer by using PEG hydrogel to create peri-duodenal space could have beneficial implications for treatment and warrants more exploration.

Clinical and molecular characteristics of patients with brain metastasis secondary to pancreatic ductal adenocarcinoma.

BACKGROUND: The prognosis for patients with pancreatic ductal adenocarcinoma (PDAC) is poor. Secondary brain metastasis (Br-M) occurs in less than 1% of patients. Clinical characteristics and molecular alterations have not been characterized in this rare patients' subset. MATERIALS AND METHODS: The Foundry software platform was used to retrospectively query electronic health records for patients with Br-M secondary to PDAC from 2005 to 2023; clinical, molecular, and overall survival (OS) data were analyzed. RESULTS: Br-M was diagnosed in 44 patients with PDAC. Median follow-up was 78 months; median OS from initial PDAC diagnosis was 47 months. Median duration from PDAC diagnosis to Br-M detection was 24 months; median OS from Br-M diagnosis was 3 months. At Br-M diagnosis, 82% (n = 36) of patients had elevated CA19-9. Lung was the most common preexisting metastatic location (71%) with Br-M, followed by liver (66%). Br-M were most frequently observed in the frontal lobe (34%, n = 15), cerebellar region (23%, n = 10), and leptomeninges (18%, n = 8). KRAS mutations were detected in 94.1% (n = 16) of patients who had molecular data available (n = 17) with KRASG12V being the most frequent subtype 47% (n = 8); KRASG12D in 29% (n = 5); KRASG12R in 18% (n = 3). Patients who underwent Br-M surgical resection (n = 5) had median OS of 8.6 months, while median OS following stereotactic radiosurgery only (n = 11) or whole-brain radiation only (n = 20) was 3.3 and 2.8 months, respectively. CONCLUSION: Br-M is a late PDAC complication, resulting in an extremely poor prognosis especially in leptomeningeal disease. KRAS was mutated in 94.1% of the patients and the KRASG12V subtype was prevalent.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Escalated-dose radiotherapy for unresected locally advanced pancreatic cancer: Patterns of care and survival in the United States.

INTRODUCTION: With locally advanced pancreatic cancer (LAPC), uncontrolled local tumor growth frequently leads to mortality. Advancements in radiotherapy (RT) techniques have enabled conformal delivery of escalated-dose RT (EDR), which may have potential local control and overall survival (OS) benefits based on retrospective and early prospective studies. With evidence for EDR emerging, we characterized the adoption of EDR across the United States and its associated outcomes. METHODS: We searched the National Cancer Database for nonsurgically managed LAPC patients diagnosed between 2004 and 2019. Pancreas-directed RT with biologically effective doses (BED10) ≥39 and ≤70 Gy was labeled conventional-dose RT (CDR), and BED10 >70 and ≤132 Gy was labeled EDR. We identified associations of EDR and OS using logistic and Cox regressions, respectively. RESULTS: Among the definitive therapy subset (n = 54,115) of the entire study cohort (n = 91,493), the most common treatments were chemotherapy alone (69%), chemotherapy and radiation (29%), and RT alone (2%). For the radiation therapy subset (n = 16,978), use of pancreas-directed RT remained between 13% and 17% over the study period (ptrend > 0.999). Using multivariable logistic regression, treatment at an academic/research facility (adjusted odds ratio [aOR] 1.46, p < 0.001) and treatment between 2016 and 2019 (aOR 2.54, p < 0.001) were associated with greater receipt of EDR, whereas use of chemotherapy (aOR 0.60, p < 0.001) was associated with less receipt. Median OS estimates for EDR and CDR were 14.5 months and 13.0 months (p < 0.0001), respectively. For radiation therapy subset patients with available survival data (n = 13,579), multivariable Cox regression correlated EDR (adjusted hazard ratio 0.85, 95% confidence interval 0.80-0.91; p < 0.001) with longer OS versus CDR. DISCUSSION AND CONCLUSIONS: Utilization of EDR has increased since 2016, but overall utilization of RT for LAPC has remained at less than one in five patients for almost two decades. These real-world results additionally provide an estimate of effect size of EDR for future prospective trials.

Phase I trial of single-photon emission computed tomography-guided liver-directed radiotherapy for patients with low functional liver volume.

BACKGROUND: Traditional constraints specify that 700 cc of liver should be spared a hepatotoxic dose when delivering liver-directed radiotherapy to reduce the risk of inducing liver failure. We investigated the role of single-photon emission computed tomography (SPECT) to identify and preferentially avoid functional liver during liver-directed radiation treatment planning in patients with preserved liver function but limited functional liver volume after receiving prior hepatotoxic chemotherapy or surgical resection. METHODS: This phase I trial with a 3 + 3 design evaluated the safety of liver-directed radiotherapy using escalating functional liver radiation dose constraints in patients with liver metastases. Dose-limiting toxicities were assessed 6-8 weeks and 6 months after completing radiotherapy. RESULTS: All 12 patients had colorectal liver metastases and received prior hepatotoxic chemotherapy; 8 patients underwent prior liver resection. Median computed tomography anatomical nontumor liver volume was 1584 cc (range = 764-2699 cc). Median SPECT functional liver volume was 1117 cc (range = 570-1928 cc). Median nontarget computed tomography and SPECT liver volumes below the volumetric dose constraint were 997 cc (range = 544-1576 cc) and 684 cc (range = 429-1244 cc), respectively. The prescription dose was 67.5-75 Gy in 15 fractions or 75-100 Gy in 25 fractions. No dose-limiting toxicities were observed during follow-up. One-year in-field control was 57%. One-year overall survival was 73%. CONCLUSION: Liver-directed radiotherapy can be safely delivered to high doses when incorporating functional SPECT into the radiation treatment planning process, which may enable sparing of lower volumes of liver than traditionally accepted in patients with preserved liver function. TRIAL REGISTRATION: NCT02626312.

Technical note: Radiological clinical equivalence for phantom materials in carbon ion therapy.

PURPOSE: As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam. METHODS: Eight materials were tested for radiological material-equivalence in a carbon ion beam. The materials were computed tomography (CT)-scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread-out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU-RLSP relationship. RESULTS: Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV). CONCLUSIONS: Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

OSLD nanoDot characterization for carbon radiotherapy dosimetry.

Objective. This study characterized optically-stimulated luminescent dosimeter (OSLD) nanoDots for use in a therapeutic carbon beam using the Imaging and Radiation Oncology Core (IROC) framework for remote output verification.Approach. The absorbed dose correction factors for OSLD (fading, linearity, beam quality, angularity, and depletion), as defined by AAPM TG 191, were characterized for carbon beams. For the various correction factors, the effect of linear energy transfer (LET) was examined by characterizing in both a low and high LET setting.Main results. Fading was not statistically different between reference photons and carbon, nor between low and high LET beams; thus, the standard IROC-defined exponential function could be used to characterize fading. Dose linearity was characterized with a linear fit; while low and high LET carbon linearity was different, these differences were small and could be rolled into the uncertainty budget if using a single linearity correction. A linear fit between beam quality and dose-averaged LET was determined. The OSLD response at various angles of incidence was not statistically different, thus a correction factor need not be applied. There was a difference in depletion between low and high LET irradiations in a primary carbon beam, but this difference was small over the standard five readings. The largest uncertainty associated with the use of OSLDs in carbon was because of thekQcorrection factor, with an uncertainty of 6.0%. The overall uncertainty budget was 6.3% for standard irradiation conditions.Significance. OSLD nanoDot response was characterized in a therapeutic carbon beam. The uncertainty was larger than for traditional photon applications. These findings enable the use of OSLDs for carbon absorbed dose measurements, but with less accuracy than conventional OSLD audit programs.

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy.

We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

Radiation Therapy Quality Assurance Analysis of Alliance A021501: Preoperative mFOLFIRINOX or mFOLFIRINOX Plus Hypofractionated Radiation Therapy for Borderline Resectable Adenocarcinoma of the Pancreas.

PURPOSE: Alliance A021501 is the first randomized trial to evaluate stereotactic body radiation therapy (SBRT) for borderline resectable pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant chemotherapy. In this post hoc study, we reviewed the quality of radiation therapy (RT) delivered. METHODS AND MATERIALS: SBRT (6.6 Gy × 5) was intended but hypofractionated RT (5 Gy × 5) was permitted if SBRT specifications could not be met. Institutional credentialing through the National Cancer Institute-funded Imaging and Radiation Oncology Core (IROC) was required. Rigorous RT quality assurance (RT QA) was mandated, including pretreatment review by a radiation oncologist. Revisions were required for unacceptable deviations. Additionally, we performed a post hoc RT QA analysis in which contours and plans were reviewed by 3 radiation oncologists and assigned a score (1, 2, or 3) based on adequacy. A score of 1 indicated no deviation, 2 indicated minor deviation, and 3 indicated a major deviation that could be clinically significant. Clinical outcomes were compared by treatment modality and by case score. RESULTS: Forty patients were registered to receive RT (1 planned but not treated) at 27 centers (18 academic and 9 community). Twenty-three centers were appropriately credentialed for moving lung/liver targets and 4 for static head and neck only. Thirty-two of 39 patients (82.1%) were treated with SBRT and 7 (17.9%) with hypofractionated RT. Five cases (13%) required revision before treatment. On post hoc review, 23 patients (59.0%) were noted to have suboptimal contours or plan coverage, 12 (30.8%) were scored a 2, and 11 (28.2%) were scored a 3. There were no apparent differences in failure patterns or surgical outcomes based on treatment technique or post hoc case score. Details related to on-treatment imaging were not recorded. CONCLUSIONS: Despite rigorous QA, we encountered variability in simulation, contouring, plan coverage, and dose on trial. Although clinical outcomes did not appear to have been affected, findings from this analysis serve to inform subsequent PDAC SBRT trial designs and QA requirements.

Trends Over Time in Recurrence Patterns and Survival Outcomes after Neoadjuvant Therapy and Surgery for Pancreatic Cancer.

OBJECTIVE: We aimed to determine if advances in neoadjuvant therapy affected recurrence patterns and survival outcomes after pancreatectomy for pancreatic ductal adenocarcinoma (PDAC). BACKGROUND: Data are limited on how modern multimodality therapy affects PDAC recurrence and post-recurrence survival. METHODS: Patients who received neoadjuvant therapy followed by curative-intent pancreatectomy for PDAC during 1998-2018 were identified. Treatments, recurrence sites and timing, and survival were compared between patients who completed neoadjuvant therapy and pancreatectomy in 1998-2004, 2005-2011, and 2012-2018. RESULTS: The study included 727 patients (203, 251, and 273 in the 1998-2004, 2005-2011, and 2012-2018 cohorts, respectively). Use of neoadjuvant induction chemotherapy increased over time, and regimens changed over time, with >80% of patients treated in 2012-2018 receiving FOLFIRINOX or gemcitabine with nab-paclitaxel. Overall, recurrence sites and incidence (67.5%, 66.1%, and 65.9%) remained stable, and 85% of recurrences occurred within 2 years of surgery. However, compared to earlier cohorts, the 2012-2018 cohort had lower conditional risk of recurrence in postoperative year 1 and higher risk in postoperative year 2. Overall survival increased over time (median, 30.6, 33.6, and 48.7 mo, P < 0.005), driven by improved post-recurrence overall survival (median, 7.8, 12.5, and 12.6 mo; 3-year rate, 7%, 10%, and 20%; P < 0.005). CONCLUSIONS: We observed changes in neoadjuvant therapy regimens over time and an associated shift in the conditional risk of recurrence from postoperative year 1 to postoperative year 2, although recurrence remained common. Overall survival and post-recurrence survival remarkably improved over time, reflecting improved multimodality regimens for recurrent disease.

Long-Term Patient-Reported Dyspareunia After Definitive Chemoradiation for Anal Cancer: Using the Anterior Vaginal Wall as an Organ-at-Risk to Define an Actionable Dosimetric Goal.

Purpose: Chemoradiation therapy (CRT) is the standard treatment for squamous cell carcinoma of the anus (SCCA). This study aimed to investigate the relationship between vaginal dosimetry and long-term patient-reported dyspareunia after treatment. We further aimed to use the anterior vaginal wall (AVW) as an organ at risk to define an actionable dosimetric clinical goal to decrease the risk of patient-reported dyspareunia. Methods and Materials: Women with SCCA treated with intensity modulated radiation therapy-based CRT were surveyed at least 2 years after successfully completing therapy. A Female Sexual Function Index (FSFI) pain subscore ≤4 was used to define dyspareunia. Dosimetric parameters were calculated for both the full vaginal canal and AVW. Multivariable linear regression models were created to identify predictors of FSFI pain subscore using backward selection to identify final variables include in the models. An actionable dosimetric predictor for dyspareunia was established using the Youden index method for cutoff optimization. Results: Of 184 women who were contacted, 90 (49%) returned completed surveys. Of those who completed surveys, 51 (56.7%) reported being sexually active, and 47 had dosimetric data available for review. Of sexually active respondents, 32 (68%) had an FSFI pain subscore ≤4. Multiple regression models were generated using the full vaginal canal and AVW as organs at risk, and both models showed similar predictive relationships with volumetric dose parameters emerging as the best dosimetric predictors for dysparenuia. Age over 65 years was also associated with higher FSFI pain subscores (eg, less pain with intercourse) in both models. AVW V35 Gy < 60% was identified as the optimal cutoff to reduce the risk of patient-reported dyspareunia. Conclusions: Increased dose to the vaginal canal is significantly associated with worse patient-reported dyspareunia following CRT for SCCA. Minimizing dose to the AVW to V35 Gy < 60% may reduce the risk of this quality of life-limiting toxicity. Further prospective evaluation is needed to validate these findings.

Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.

Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), 40 ([Formula: see text]), 33 ([Formula: see text]), 25 (CCH) and 20 (CPVE) CECT of LC patients. [Formula: see text] outperformed [Formula: see text] across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03-0.05 (p < 0.05). [Formula: see text], and [Formula: see text] were not statistically different (p > 0.05), however, both were slightly better than [Formula: see text] by DSC up to 0.02. The final model, [Formula: see text], showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5-8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score [Formula: see text] 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning.

Defining Minimum Treatment Parameters of Ablative Radiation Therapy in Patients With Hepatocellular Carcinoma: An Expert Consensus.

PURPOSE: External beam radiation therapy (EBRT) is a highly effective treatment in select patients with hepatocellular carcinoma (HCC). However, the Barcelona Clinic Liver Cancer system does not recommend the use of EBRT in HCC due to a lack of sufficient evidence and intends to perform an individual patient level meta-analysis of ablative EBRT in this population. However, there are many types of EBRT described in the literature with no formal definition of what constitutes "ablative." Thus, we convened a group of international experts to provide consensus on the parameters that define ablative EBRT in HCC. METHODS AND MATERIALS: Fundamental parameters related to dose, fractionation, radiobiology, target identification, and delivery technique were identified by a steering committee to generate 7 Key Criteria (KC) that would define ablative EBRT for HCC. Using a modified Delphi (mDelphi) method, experts in the use of EBRT in the treatment of HCC were surveyed. Respondents were given 30 days to respond in round 1 of the mDelphi and 14 days to respond in round 2. A threshold of ≥70% was used to define consensus for answers to each KC. RESULTS: Of 40 invitations extended, 35 (88%) returned responses. In the first round, 3 of 7 KC reached consensus. In the second round, 100% returned responses and consensus was reached in 3 of the remaining 4 KC. The distribution of answers for one KC, which queried the a/b ratio of HCC, was such that consensus was not achieved. Based on this analysis, ablative EBRT for HCC was defined as a BED10 ≥80 Gy with daily imaging and multiphasic contrast used for target delineation. Treatment breaks (eg, for adaptive EBRT) are allowed, but the total treatment time should be ≤6 weeks. Equivalent dose when treating with protons should use a conversion factor of 1.1, but there is no single conversion factor for carbon ions. CONCLUSIONS: Using a mDelphi method assessing expert opinion, we provide the first consensus definition of ablative EBRT for HCC. Empirical data are required to define the a/b of HCC.