TPS1098 Background: This randomized Phase II trial evaluates if stereotactic body radiotherapy (SBRT) and/or surgical resection (SR) of all metastatic sites in newly oligo-metastatic breast cancer who have received, or planned to receive, up to 12 months of first line systemic therapy without progression will significantly improve median progression free survival (PFS). If this aim is met, a phase III component evaluates if SBRT/SR improves 5 year OS. Secondary aims include local control in the metastases, new metastases, and technical quality. Translational primary endpoint is to determine whether < 5 CTCs is an independent prognostic marker for improved PFS and OS. Methods: A 2016 amendment expanded eligibility criteria to pathologically confirmed metastatic breast cancer to ≤ 2 sites and < 12 months of standard first line systemic therapy. Registration must occur <365 days of initial metastatic diagnosis. Primary disease must be controlled prior to registration. CNS metastases are ineligible. ER/PR and HER-2 neu is required on either the primary or metastatic site. Randomization is to standard systemic therapy with local radiotherapy/ surgery for palliation when necessary versus ablative therapy of all metastases with SBRT and/or SR. Statistics: For the phase IIR portion to detect a signal for improved median PFS from 10.5 months to 19 months with 95% power, 146 patients are required. The Phase III requires an additional 246 patients to definitively determine if ablative therapy improves 5-year overall survival from 28% to 42.5% (HR = 0.67), with 85% power and one-sided type I error of 0.025. For the translational research assuming a two-sided probability of type I error of 0.05, patients accrued in the Phase II-R/III portions will provide sufficient power of at least 91% to detect whether < 5 CTC's is prognostic. Contact Information: Protocol: CTSU member web site https://www.ctsu.org. Pt enrollment: OPEN at https://open.ctsu.org or the OPEN tab on CTSU member web site. Support: Supported by NRG Oncology grants U10CA180868 and U10CA180822 from the National Cancer Institute (NCI). Clinical trial information: NCI-2014-01810.
e20525 Background: Treatment for inoperable stage II-III non-small cell lung cancer (NSCLC) involves aggressive chemo-radiotherapy (CRT). While outcomes have improved with immunotherapy, some patients transition to hospice or die early in their treatment course. To help identify these patients, we developed a predictive model for early poor outcomes in NSCLC patients treated with curative intent. Methods: In a statewide consortium involving 27 sites, information was collected prospectively on stage II-III NSCLC patients who received curative CRT from April 2012 to November 2019. We defined an early poor outcome as termination of treatment due to hospice enrollment or death within 5 months of initiating radiation therapy. Potential predictors included clinical characteristics and patient reported outcomes (PROs) from validated questionnaires. Logistic regression models were used to assess potential predictors and build predictive models. Multiple imputation was used to handle missing data. We used Lasso regularized logistic regression to build a predictive model with multiple predictor variables. Results: Of the total of 2267 included patients, 128 patients discontinued treatment early due to hospice enrollment or death. The mean age of the 128 patients was 71 years old (range 48-91) and 59% received concurrent chemotherapy. Significant uni-variable predictors of early hospice or death were advanced age, worse ECOG performance status, high PTV volume, short distance to normal tissue critical structures, high mean heart dose, uninsured status, lower scores on the Functional and Physical Well-Being scale and the Lung Cancer Symptoms sub-scale of the FACT-L quality of life instrument, as well as higher levels of patient-reported lack of energy, cough, and shortness of breath. The best predictive model included age, ECOG performance status, PTV volume, mean heart dose, patient insurance status, and patient-reported lack of energy and cough. The pooled estimate of area under the curve (AUC) for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%. Conclusions: Our models identified a combination of clinical variables and PROs that may help identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at a high risk of early hospice enrollment or death. These preliminary results are encouraging and warrant further evaluation in a larger cohort of patients.
Purpose: Apertures optimized during VMAT planning can be small and irregular, resulting in dosimetric inaccuracies during delivery. Our purpose is to develop and integrate an aperture‐regularization objective function into the optimization process for VMAT, and to quantify the impact of using this objective function on dose delivery accuracy and optimized dose distributions. Methods: An aperture‐based metric (‘edge penalty’) was developed that penalizes complex aperture shapes based on the ratio of MLC side edge length and aperture area. To assess the utility of the metric, VMAT plans were created for an example paraspinal SBRT case with and without incorporating the edge penalty in the cost function. To investigate the dose accuracy, Gafchromic EBT2 film was used to measure 15 sample individual apertures and composite plans with and without the edge penalty applied. Films were analyzed using a triple‐channel uniformity correction and measurements were compared directly to calculations. Results: Apertures generated with the edge penalty were larger, more regularly shaped and required 22% fewer monitor units than those created without the edge penalty. DVH analysis showed that the changes in doses to organs at risk and normal tissues were minimal. Edge penalty apertures showed a significant decrease in the number of pixels disagreeing with calculation by more than 10%. The number of pixels passing in the composite dose distributions for the edge penalty and non‐edge penalty plans were 52% and 96%, respectively. Employing gamma criteria of 3%/1mm resulted in a 79.5% (without penalty) / 95.4% (with penalty) pass rate for the two plans. Conclusions: The use of the edge penalty during optimization has the potential to significantly improve dose delivery accuracy for VMAT plans while minimally affecting optimized dose distributions.
Abstract Purpose Advanced radiotherapy delivery systems designed for high‐dose, high‐precision treatments often come equipped with high‐definition multi‐leaf collimators ( HD ‐ MLC ) aimed at more finely shaping radiation dose to the target. In this work, we study the effect of a high definition MLC on spine stereotactic body radiation therapy ( SBRT ) treatment plan quality and plan deliverability. Methods and Materials Seventeen spine SBRT cases were planned with VMAT using a standard definition MLC (M120), HD ‐ MLC , and HD ‐ MLC with an added objective to reduce monitor units ( MU ). M120 plans were converted into plans deliverable on an HD ‐ MLC using in‐house software. Plan quality and plan deliverability as measured by portal dosimetry were compared among the three types of plans. Results Only minor differences were noted in plan quality between the M120 and HD ‐ MLC plans. Plans generated with the HD ‐ MLC tended to have better spinal cord sparing (3% reduction in maximum cord dose). HD ‐ MLC plans on average had 12% more MU and 55% greater modulation complexity as defined by an in‐house metric. HD ‐ MLC plans also had significantly degraded deliverability. Of the VMAT arcs measured, 94% had lower gamma passing metrics when using the HD ‐ MLC . Conclusion Modest improvements in plan quality were noted when switching from M120 to HD ‐ MLC at the expense of significantly less accurate deliverability in some cases.
Abstract Introduction: Stereotactic body radiation therapy (SBRT) for the treatment of hepatocellular carcinoma (HCC) remains a challenge due to high rates of toxicity in patients with impaired liver function or tumors not amenable to thermal ablation. We performed a single-arm prospective phase II clinical trial utilizing a novel treatment paradigm optimizing the utility of SBRT based on the individual patient’s probability for tumor control traded off against the risk of liver injury. We hypothesized that maximizing the utility of treatment would decrease toxicity while achieving the same tumor control rate as standard therapy. Methods: Patients with Child-Pugh (CP) A to B7 disease with tumors >3.5 cm, or CP ≥ B8 with any size tumor were prospectively enrolled on an IRB approved clinical trial to undergo SBRT with baseline dose optimization and mid-treatment response adaptation. Optimization and adaptation were based on the expected utility of treatment, calculated as a weighted average of the probability of 4 combinations of toxicity and efficacy outcomes. These calculations were based on the individual patient’s baseline indocyanine green retention at 15 minutes or albumin-bilirubin score (ALBI), CP score, intended dose and fractionation, and mean liver dose with the goal of maximizing the difference between the probability of local control compared to the probability of treatment-related toxicity. Primary endpoints were rate of liver decompensation as measured by ≥2 point change in CP score within 6 months, and lesion-specific local control. Overlap weighting was used to compare patients treated on protocol with patients receiving conventional SBRT at another high-volume cancer center. Results: 56 patients with 80 tumors met inclusion criteria and had a median follow-up of 11.2 months. 44 tumors with a median size of 3.8 cm were treated in CP-A to B7 patients, while 36 tumors with a median size of 2.1 cm were treated in CP ≥ B8 patients. Optimization resulted modification of initial dose for 38% of patients. Sixty-eight percent of patients underwent mid-treatment adaptation with either omission or dose reduction of the final two treatments based on change in expected utility. The 1 year freedom from local progression was 94%. A total of 21% of patients experienced a ≥ 2 point change in CP score within 6 months. Overlap weighted analysis revealed similar local control (HR 0.69, 95% CI [0.25-1.91], p = 0.48), and overall survival (HR 1.45, 95% CI [0.69-3.0], p = 0.33), with decreased toxicity (OR 0.26, 95% CI [0.07 - 0.99], p = 0.048) compared to conventional SBRT. Conclusion: SBRT for HCC patients with large tumors or poor liver function can be optimized via an individualized, utility-based treatment paradigm which may decrease treatment-related toxicity while maintaining tumor control. Citation Format: Daniel J. Herr, Chang Wang, Mishal Mendiratta-Lala, Martha Matuszak, Charles S. Mayo, Yue Cao, Neehar Parikh, Randy Ten Hanken, Dawn Owen, Teodor Stanescu, Michael Yan, Laura A. Dawson, Matthew Schipper, Theodore S. Lawrence, Kyle C. Cuneo. A phase II study of optimized individualized adaptive radiation therapy for hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT151.
We combined clinical practice changes, standardizations, and technology to automate aggregation, integration, and harmonization of comprehensive patient data from the multiple source systems used in clinical practice into a big data analytics resource system (BDARS). We then developed novel artificial intelligence algorithms, coupled with the BDARS, to identify structure dose volume histograms (DVH) metrics associated with dysphagia.From the BDARS harmonized data of ≥22,000 patients, we identified 132 patients recently treated for head and neck cancer who also demonstrated dysphagia scores that worsened from base line to a maximum grade ≥2. We developed a method that used both physical and biologically corrected (α/β = 2.5) DVH curves to test both absolute and percentage volume based DVH metrics. Combining a statistical categorization algorithm with machine learning (SCA-ML) provided more extensive detailing of response threshold evidence than either approach alone. A sensitivity guided, minimum input, machine learning (ML) model was iteratively constructed to identify the key structure DVH metric thresholds.Seven swallowing structures producing 738 candidate DVH metrics were ranked for association with dysphagia using SCA-ML scoring. Structures included superior pharyngeal constrictor (SPC), inferior pharyngeal constrictor (IPC), larynx, and esophagus. Bilateral parotid and submandibular gland (SG) structures were categorized by relative mean dose (eg, SG_high, SG_low) as a dose versus tumor centric analog to contra and ipsilateral designations. Structure DVH metrics with high SCA-ML scores included the following: SPC: D20% (equivalent dose [EQD2] Gy) ≥47.7; SPC: D25% (Gy) ≥50.4; IPC: D35% (Gy) ≥61.7; parotid_low: D60% (Gy) ≥13.2; and SG_high: D35% (Gy) ≥61.7. Larynx: D25% (Gy) ≥21.2 and SG_low: D45% ≥28.2 had high SCA-ML scores but were segmented on less than 90% of plans. A model based on SPC: D20% (EQD2 Gy) alone had sensitivity and area under the curve of 0.88 ± 0.13 and 0.74 ± 0.17, respectively.This study provides practical demonstration of combining big data with artificial intelligence to increase volume of evidence in clinical learning paradigms.
