Yu, Nathan Y.

Abstract Background Radiotherapy of breast cancer can be associated with dermatitis. Grade ≥ 2 radiation dermatitis can be painful and impair the patients’ quality of life. To reduce the risk of this complication, patients have to perform skin care several times each day. This may require a considerable level of compliance. This randomized DAI-BREAC trial investigates whether a mobile application reminding the patients four times per day to perform skin care (reminder app) will contribute to reduction of grade ≥ 2 radiation dermatitis. Methods This multinational, randomized, active-controlled, parallel-group, multicenter trial compares standard skin care supported by a reminder app (Arm A) versus standard skin care alone (Arm B) regarding grade ≥ 2 radiation dermatitis in patients receiving adjuvant radiotherapy for breast cancer. The effect of the app will be considered clinically relevant, if the rate of grade ≥ 2 radiation dermatitis is reduced from 25.4% (rate identified in a preceding study) to 15%. A total of 134 patients per arm including drop-outs are required. Secondary aims include pain (visual analogue scale), patient satisfaction with the app (questionnaire), impact of the app on the use of health technology (questionnaire), and benefit from support (coaching) by staff members regarding the use of the app (questionnaire). Discussion If reminder app contributes to a decrease of grade ≥ 2 radiation dermatitis in patients irradiated for breast cancer, it will likely become a useful instrument for these patients. Trial registration: clinicaltrials.gov (NCT06483477; URL: https://clinicaltrials.gov/show/NCT06483477). Registered on 1st of July, 2024. First patient is planned to be included in September 2024.

Many patients with malignant spinal cord compression (MSCC) who are not candidates for neurosurgery receive radiotherapy alone. This study compared 15 × 2.633 Gy over three weeks to 20 × 2.0 Gy over four weeks in patients with favorable survival prognoses. The outcomes of 34 patients treated with 15 × 2.633 Gy (equivalent dose 41.6 Gy10) in the RAMSES-01 trial were compared to 239 patients from an existing database receiving 20 × 2.0 Gy using propensity-score-adjusted Cox and logistic regression models. All patients had favorable survival prognoses. Endpoints included local progression-free survival (LPFS), improvement of motor function, post-treatment ambulatory status, and overall survival (OS). After propensity score adjustment, the 12-month rates of LPFS and OS were 98.1% (RAMSES-01 cohort) vs. 91.6% (p = 0.265) and 79.1% vs. 82.2% (p = 0.704), respectively. Regarding improvement of motor function, 15 × 2.633 Gy appeared superior (p = 0.073). No significant difference was observed regarding ambulatory status (p = 0.822). The three-week regimen for MSCC has similar outcomes and reduces treatment time when compared to a four-week regimen.

Abstract Background: Pneumonitis caused by radiotherapy for lung cancer may be missed, since it often occurs only several months later. In a previous trial including patients of any age, a symptom-based scoring system was tested to facilitate the correct diagnosis of radiation pneumonitis. Since elderly lung cancer patients have a greater risk of developing this complication, a separate scoring system particularly for this age group appears reasonable. This trial investigates such a specific tool for elderly patients irradiated for lung cancer. Methods: Patients with lung cancer patients aged ≥65 years will be included in this prospective multi-center trial and complete paper-based questionnaires (symptom-based scoring system). Patients indicate and rate symptoms potentially caused by pneumonitis, namely cough, dyspnea, and fever, once a week during and up to 24 weeks following their radiotherapy course. If symptomatic (grade ≥2) pneumonitis is confirmed by corresponding diagnostic procedures, patients receive prednisolone whenever possible. The total score of the symptom-based scoring system, which ranges between 0 and 9 points, is correlated to pneumonitis (yes or no). The discriminative power of the scoring system is evaluated by calculating the area under the receiver operating characteristic curve (AUC). Optimality is defined as a cut-off score with sensitivity ≥90% and specificity ≥80%. In addition, the Youden index (sensitivity + specificity – 1) will be applied. Fifty-nine patients (18 with and 41 without pneumonitis) are required in the full analysis set. Assuming that 5% of the patients will not qualify for this set, 62 patients should be enrolled. In addition, patient satisfaction with the scoring system is evaluated. If the dissatisfaction rate is >20%, the score needs modifications; if the dissatisfaction rate is >40%, the scoring system is considered not useful. Discussion: If the optimal cut-off score facilitates the diagnosis of radiation pneumonitis and its discrimination from other lung diseases in elderly patients with lung cancer, it will contribute to the development of a mobile application for identification of radiation pneumonitis, which can be used by the patients at home. Trial registration: clinicaltrials.gov (NCT06480734; URL: https://clinicaltrials.gov/show/NCT06480734); protocol version 1.2, registered on 27th of June, 2024.

AbstractPurposeTo assess the practicality of employing a commercial knowledge‐based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone‐beam computed tomography (iCBCT) datasets.Methods and materialsIntact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose‐volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan‐generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow.ResultsModel testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09 ± 02:06 (intact) and 07:12 ± 01:04 (bed).ConclusionThis study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

11118 Background: Lung cancer survivors experience more physical and mental challenges, heavier symptom burden, and poorer quality of life than survivors of other cancers. Many symptoms persist or worsen for survivors beyond 5 years of cancer diagnosis, referred as long-term lung cancer survivors (LTLCS). Social determinants of health (SDoH) factors negatively impact patient reported outcomes (PROs) among cancer survivors, but their role in outcomes is under reported in lung cancer survivors and unknown in LTLCS. We investigated the impact of SDoH on PROs in a 20-year prospective lung cancer cohort. Methods: Our cohort includes 19,251 lung cancer patients (cases) diagnosed from 1997 through 2016 and followed up to 27 years, of whom 4799 (25%) became LTLCS. SDoH information was obtained by a lung cancer study PRO questionnaire or an institution SDoH questionnaire. Analyzed in this report are 2982 cases and 480 controls who had a low-dose chest CT scan and were ruled out for cancer, all answered at least one PRO questionnaire (first response); and 1366 LTLCS answered 5 years after diagnosis (long-term response). Along with fatigue and dyspnea, 5 SDoH variables, i.e., financial concerns, legal concerns, family and friends support, spiritual well-being, and social activity were scored on a 0 (worst) - 10 (best) scale and dichotomized as 0-5 (deficit/worse/lower) or 6-10 (no deficit/better/higher). We also assessed employment and health-related work experience. A difference of 1.0 unit or more in score was considered clinically meaningful along with a 2-sided test P-value less than 0.05; Chi-square or Fisher’s Exact Test and Kruskal-Wallis test were used accordingly. Results: Compared to controls, cases were younger (mean age: 62 vs. 65 years, P<0.05); more women than men reported worse financial concerns (56% vs. 38%, P<0.01) and lower spiritual well-being (43% vs. 26%, P=0.04) at first response; this sex disparity reduced but remained significant at long-term. Worse dyspnea (4.4-4.8 vs. 5.6) and fatigue (4.7-4.8 vs. 5.8-5.9) were seen in cases with more financial and legal concerns compared to those with fewer concerns (Ps<0.01). Cases with higher family and friends support, spiritual well-being and social activity had less dyspnea (5.7-6.0 vs. 4.2-4.5) and fatigue (5.5-5.9 vs. 3.9-4.4) than those with lower scores (Ps<0.04). Employment and health-related work experience data were provided by 2213 responders: Comparing 1735 cases to 478 controls, a lower proportion of cases was employed (28% vs. 39%), more cases “often felt tired after work” (49% vs. 28%), and more cases had “quite a lot” of difficulty in daily work or could not work at all” (26% vs. 9%, Ps<0.01). Conclusions: We found that in lung cancer survivors, more women than men reported worse financial concerns, and lower SDoH status was correlated with worse dyspnea and fatigue, indicating SDoH-driven interventions are important to improve lung cancer survivors’ PROs.

AbstractBackgroundAccurate and efficient dose calculation is essential for on‐line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL‐based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities.PurposeTo develop a DL‐based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on‐line adaptive proton therapy clinical decision and subsequent replanning.MethodsPBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in‐house developed Monte‐Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs‐at‐risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D‐Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso‐dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency.ResultsCompared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s.ConclusionsAn accurate and efficient deep learning‐augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

Abstract Background Immune checkpoint inhibitor combined with platinum‐etoposide is the standard first‐line therapy for patients with extensive‐stage small cell lung cancer (ES‐SCLC). The phase 3 clinical trials that led to the approval of chemoimmunotherapy in ES‐SCLC excluded patients who had an Eastern Cooperative Group (ECOG) performance status (PS) of 2–3. Therefore, data on the efficacy of chemoimmunotherapy in patients with an ECOG PS of 2–3 are limited. Methods A retrospective analysis was performed on patients diagnosed with ES‐SCLC who received chemoimmunotherapy (atezolizumab or durvalumab) within the Mayo Clinic Health System between January 2016 and January 2021. The objective of this study was to compare the overall survival (OS), progression‐free survival (PFS), and best clinical response to therapy in patients with an ECOG PS of 0–1 vs. patients with an ECOG PS of 2–3 who received chemoimmunotherapy for newly diagnosed ES‐SCLC. Results In total, 82 patients were included in the study. The mean ± standard deviation age was 68.1 ± 8.3 years. Of these, 56 patients were identified with an ECOG PS of 0–1, and 26 patients were identified with an ECOG PS of 2–3. The median PFS was similar regardless of ECOG PS (5.8 months [95% CI, 4.3–6.0 months] in the ECOG PS 0–1 group vs. 4.1 months [95% CI, 3.8–6.9 months] in the ECOG PS 2–3; p = .2994). The median OS was also similar regardless of ECOG PS (10.6 months [95% CI, 8.4–13.4 months] in the ECOG PS 0–1 group vs. 9.3 months [95% CI, 4.9–12.8 months]; p = .2718) in the ECOG PS 2‐3 group. Conclusions The study results demonstrated no significant difference in PFS or OS among the ECOG PS 2–3 and ECOG PS 0–1 groups. Therefore, chemoimmunotherapy should be considered for patients who have ES‐SCLC with an ECOG PS of 2–3.

Many head and neck cancer patients assigned to definitive or adjuvant chemoradiation treatment do not complete the concurrent cisplatin dose. We determined corresponding risk factors and developed a prognostic instrument to help identify these patients. Ten pre-treatment characteristics were retrospectively analyzed in 154 patients with head and neck cancer who were treated via chemoradiation with cisplatin. These pre-treatment characteristics included age, sex, Karnofsky performance score, tumor site, primary tumor stage, nodal stage, histologic grade, upfront surgery, human papilloma virus status, and history of smoking. The characteristics significantly associated with the completion of cisplatin-based treatment, the receipt of ≥80% cisplatin, or showing a strong trend of association after multivariate analyses were used for the prognostic instrument. For each characteristic, 0 points were assigned for worse outcomes, and 1 point was assigned for better outcomes. Patients’ scores were calculated by adding these points. Age ≤ 60 years and a Karnofsky performance score of 90–100 were significantly associated with both endpoints after multivariate analysis, and male gender showed a trend for association with the receipt of ≥80% cisplatin. Patient scores were 0, 1, 2, and 3 points. The corresponding rates of completion of cisplatin-based treatment were 14%, 41%, 62%, and 72%, respectively (p = 0.004). The rates of receipt of ≥80% cisplatin were 29%, 54%, 72%, and 94%, respectively (p < 0.001). This new prognostic instrument helps to predict whether head and neck cancer patients scheduled for chemoradiation will receive cisplatin as planned.