Background: The Prognostic Nutritional Index (PNI) is a parameter of nutritional and inflammation status related to toxicity in cancer treatment. Since data for head and neck cancer are scanty, this study aims to investigate the association between PNI and acute and late toxicity for this malignancy. Methods: A retrospective cohort of 179 head and neck cancer patients treated with definitive radiotherapy with induction/concurrent chemotherapy was followed-up (median follow-up: 38 months) for toxicity and vital status between 2010 and 2017. PNI was calculated according to Onodera formula and low/high PNI levels were defined according to median value. Odds ratio (OR) for acute toxicity were calculated through logistic regression model; hazard ratios (HR) for late toxicity and survival were calculated through the Cox proportional hazards model. Results: median PNI was 50.0 (interquartile range: 45.5–53.5). Low PNI was associated with higher risk of weight loss > 10% during treatment (OR = 4.84, 95% CI: 1.73–13.53 for PNI < 50 versus PNI ≥ 50), which was in turn significantly associated with worse overall survival, and higher risk of late mucositis (HR = 1.84; 95% CI:1.09–3.12). PNI predicts acute weight loss >10% and late mucositis. Conclusions: PNI could help clinicians to identify patients undergoing radiotherapy who are at high risk of acute and late toxicity.

To retrospectively evaluate the difference in terms of pathologic complete response (pCR) according to time elapsed between chemoradiation (CRT) and total mesorectal excision (TME) on a large unselected real-life dataset of locally advanced rectal cancer (LARC) patients. A multicentre retrospective cohort study of LARC patients from 21 Italian Radiotherapy Institutions was performed. Patients were stratified into 3 different time intervals from CRT. The 1st group included 300 patients who underwent TME within 6 weeks, the 2nd 1598 patients (TME within 7-12 weeks) and the 3rd 196 patients (TME within 13 or more weeks after CRT), respectively. Data on 2094 LARC patients treated between 1997 and 2016 were considered suitable for analysis. Overall, 578 patients had stage II while 1516 had stage III histological proven invasive rectal adenocarcinoma. A CRT schedule of one agent (N = 1585) or 2-drugs (N = 509) was administered. Overall, pCR was 22.3% (N = 468 patients). The proportion of patients achieving pCR with respect to time interval was, as follows: 12.6% (1st group), 23% (2nd group) and 31.1% (3rd group) (p < 0.001), respectively. The pCR relative risk comparison of 2nd to 1st group was 1.8, while 3rd to 2nd group was 1.3. Moreover, between the 3rd and 1st group, a pCR relative risk of 2.4 (p < 0.01) was noted. At univariate analysis, clinical stage III (p < 0.001), radiotherapy dose >5040 cGy (p = 0.002) and longer interval (p < 0.001) were significantly correlated to pCR. The positive impact of interval (p < 0.001) was confirmed at multivariate analysis as the only correlated factor. We confirmed on a population-level that lengthening the interval (>13 weeks) from CRT to surgery improves the pathological response (pCR and pathologic partial response; pPR) in comparison to historic data. Furthermore, radiotherapy dose >5040 cGy and two drugs chemotherapy correlated with pPR rate.

Purpose: When autocontouring based on artificial intelligence (AI) is used in the radiotherapy (RT) workflow, the contours are reviewed and eventually adjusted by a radiation oncologist before an RT treatment plan is generated, with the purpose of improving dosimetry and reducing both interobserver variability and time for contouring. The purpose of this study was to evaluate the results of application of a commercial AI-based autocontouring for RT, assessing both geometric accuracies and the influence on optimized dose from automatically generated contours after review by human operator. Materials and Methods: A commercial autocontouring system was applied to a retrospective database of 40 patients, of which 20 were treated with radiotherapy for prostate cancer (PCa) and 20 for head and neck cancer (HNC). Contours resulting from AI were compared against AI contours reviewed by human operator and human-only contours using Dice similarity coefficient (DSC), Hausdorff distance (HD), and relative volume difference (RVD). Dosimetric indices such as D_mean, D_0.03cc, and normalized plan quality metrics were used to compare dose distributions from RT plans generated from structure sets contoured by humans assisted by AI against plans from manual contours. The reduction in contouring time obtained by using automated tools was also assessed. A Wilcoxon rank sum test was computed to assess the significance of differences. Interobserver variability of the comparison of manual vs. AI-assisted contours was also assessed among two radiation oncologists for PCa. Results: For PCa, AI-assisted segmentation showed good agreement with expert radiation oncologist structures with average DSC among patients ≥ 0.7 for all structures, and minimal radiation oncology adjustment of structures (DSC of adjusted versus AI structures ≥ 0.91). For HNC, results of comparison between manual and AI contouring varied considerably e.g., 0.77 for oral cavity and 0.11–0.13 for brachial plexus, but again, adjustment was generally minimal (DSC of adjusted against AI contours 0.97 for oral cavity, 0.92–0.93 for brachial plexus). The difference in dose for the target and organs at risk were not statistically significant between human and AI-assisted, with the only exceptions of D_0.03cc to the anal canal and D_mean to the brachial plexus. The observed average differences in plan quality for PCa and HNC cases were 8% and 6.7%, respectively. The dose parameter changes due to interobserver variability in PCa were small, with the exception of the anal canal, where large dose variations were observed. The reduction in time required for contouring was 72% for PCa and 84% for HNC. Conclusions: When an autocontouring system is used in combination with human review, the time of the RT workflow is significantly reduced without affecting dose distribution and plan quality.

Preoperative radiotherapy (RT) in combination with fluoropyrimidine-based chemotherapy (CT) is the standard of care in patients with locally advanced, T3-T4 N0–2, rectal cancer (LARC). Given the correlation between RT dose-tumor response and the prognostic role of the tumor regression grade (TRG), treatment intensification represents an area of active investigation. The aim of the study was to analyze the role of RT dose-intensification in the preoperative treatment of LARC in terms of feasibility, efficacy and toxicity. We retrospectively analyzed patients with LARC treated with intensity-modulated radiotherapy (IMRT) and simultaneous integrated boost (SIB) at five Italian radiation oncology centers. Concurrent Capecitabine was administered. Treatment response was evaluated in terms of disease down-staging and TRG. Acute toxicity was evaluated according to the CTC-AE 4.0 scale. A total of 76 patients were identified for this analysis. A dose of 45 Gy was prescribed to the entire mesorectum and pelvic lymph nodes with a median SIB dose of 54 Gy (range 52.5–57.5) to the tumor and corresponding mesorectum. Overall, 74/76 (97.4%) patients completed the planned RT, whereas 64/76 (84.2%) patients completed the prescribed CT. Eight (10.5%) patients developed grade 3–4 acute toxicity. Overall, 72/76 patients underwent surgery. The tumor and nodal down-staging was documented in 51 (70.8%) and 43 (67%) patients, respectively. Twenty (27.8%) patients obtained a pathologic complete response. Surgical morbidity was reported in 13/72 patients (18.1%). Although retrospective in design, this study indicates that IMRT-SIB with a dose range of 52.5–57.5 Gy (median 54 Gy) and concomitant Capecitabine appears feasible, well tolerated and effective in terms of disease down-staging and pathological complete response. Long-term toxicity and the impact on disease control and patient survival will be evaluated with a longer follow-up time. NA