Alessandra Guido

Different options for locally advanced pancreatic cancer (LAPC) are available based on international guidelines: chemotherapy (CHT), chemoradiation (CRT), and stereotactic body radiotherapy (SBRT). However, the role of radiotherapy is debated in LAPC. We retrospectively compared CHT, CRT, and SBRT ± CHT in a real-world setting in terms of overall survival (OS), local control (LC), and distant metastasis-free survival (DMFS). LAPC patients from a multicentric retrospective database were included (2005–2018). Survival curves were calculated using the Kaplan–Meier method. Multivariable Cox analysis was performed to identify predictors of LC, OS, and DMFS. Of the 419 patients included, 71.1% were treated with CRT, 15.5% with CHT, and 13.4% with SBRT. Multivariable analysis showed higher LC rates for CRT (HR: 0.56, _95%CI 0.34–0.92, p = 0.022) or SBRT (HR: 0.27, _95%CI 0.13–0.54, p < 0.001), compared to CHT. CRT (HR: 0.44, _95%CI 0.28–0.70, p < 0.001) and SBRT (HR: 0.40, _95%CI 0.22–0.74, p = 0.003) were predictors of prolonged OS with respect to CHT. No significant differences were recorded in terms of DMFS. In selected patients, the addition of radiotherapy to CHT is still an option to be considered. In patients referred for radiotherapy, CRT can be replaced by SBRT considering its duration, higher LC rate, and OS rate, which are at least comparable to that of CRT.

Background: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. Methods: Forty-three patients under treatment in the IRCCS Sant’Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. Results: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. Conclusions: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.

Background: In our department, we provided guidelines to the radiation oncologists (ROs) regarding the omission, delay, or shortening of radiotherapy (RT). The purpose was to reduce the patients’ exposure to the hospital environment and to minimize the departmental overcrowding. The aim was to evaluate the ROs’ compliance to these guidelines. Methods: ROs were asked to fill out a data collection form during patients’ first visits in May and June 2020. The collected data included the ROs’ age and gender, patient age and residence, RT purpose, treated tumor, the dose and fractionation that would have been prescribed, and RT changes. The chi-square test and binomial logistic regression were used to analyze the correlation between the treatment prescription and the collected parameters. Results: One hundred and twenty-six out of 205 prescribed treatments were included in this analysis. Treatment was modified in 61.1% of cases. More specifically, the treatment was omitted, delayed, or shortened in 7.9, 15.9, and 37.3% of patients, respectively. The number of delivered fractions was reduced by 27.9%. A statistically significant correlation (p = 0.028) between younger patients’ age and lower treatment modifications rate was recorded. Conclusion: Our analysis showed a reasonably high compliance of ROs to the pandemic-adapted guidelines. The adopted strategy was effective in reducing the number of admissions to our department.

The study aimed to generate a local failure (LF) risk map in resected pancreatic cancer (PC) and validate the results of previous studies, proposing new guidelines for PC postoperative radiotherapy clinical target volume (CTV) delineation. Follow-up computer tomography (CT) of resected PC was retrospectively reviewed by two radiologists identifying LFs and plotting them on a representative patient CT scan. The percentages of LF points randomly extracted based on CTV following the RTOG guidelines and based on the LF database were 70% and 30%, respectively. According to the Kernel density estimation, an LF 3D distribution map was generated and compared with the results of previous studies using a Dice index. Among the 64 resected patients, 59.4% underwent adjuvant treatment. LFs closer to the root of the celiac axis (CA) or the superior mesenteric artery (SMA) were reported in 32.8% and 67.2% cases, respectively. The mean (± standard deviation) distances of LF points to CA and SMA were 21.5 ± 17.9 mm and 21.6 ± 12.1 mm, respectively. The Dice values comparing our iso-level risk maps corresponding to 80% and 90% of the LF probabilistic density and the CTVs-80 and CTVs-90 of previous publications were 0.45–0.53 and 0.58–0.60, respectively. According to the Kernel density approach, a validated LF map was proposed, modeling a new adjuvant CTV based on a PC pattern of failure.

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10⁻⁵; AUC = 0.90 (95%CI, 0.73–0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.

Background/Aim: Radiotherapy is a treatment option for both adjuvant and neo-adjuvant settings for biliary tract cancer. Guidelines on the delineation of the target volume of lymph nodes are lacking; only generic indications are available, without specific recommendations for different primary tumour locations (e.g. intrahepatic, extrahepatic biliary tract or gallbladder cancer). The aim of this study was to systematically review available literature to provide recommendations on lymph node target volume delineation in patients with unresectable biliary tumour. Materials and Methods: A systematic search of electronic databases was performed up to July 2016. The primary outcome measure was the rate of lymph node involvement according to the location of primary biliary tumour. Sites with ≥5% of nodal metastases were considered in the clinical target volume for radiotherapy planning. Results: Twelve studies (1075 patients) were included. The most frequent site of lymph node metastasis in intrahepatic biliary tree carcinoma was retroportal (61.1%, 95% confidence interval (CI)=50.7-70-6%). Other frequently involved lymph nodes were along the hepatoduodenal ligament [frequency=38.7%, 95% CI=31.0-47.0%], those along the common hepatic artery (17.0%, 95% CI=8.2-31.9%) and the hilar nodes (16.9%, 95% CI=13.2-21.4%). In extrahepatic biliary tree cancer, the most frequently involved lymph nodes were the pericholedochal (42.7%, 95% CI=33.8-52.1%) and those along the hepatoduodenal ligament (40.3%, 95% CI=32.4-48.8%). Other commonly involved nodal regions included retroportal lymph nodes (30.9%, 95% CI=23.0-40.1%), pancreaticoduodenal anterior and posterior nodes (30.1%, 95% CI=12.2-57.1%), those along the common hepatic artery (19.7%, 95% CI=11.8-31.0%) and para_aortic nodes (15.2%, 95% CI=8.0-27.0%). The most common site of metastases in gallbladder cancer were the pericholedochal nodes (25.2%, 95% CI=18.6-33.2%), those along the cystic duct (23%, 95% CI=16.6-30.8%), and retroportal nodes (17.1%, 95% CI=11.6-24.5%). Conclusion: Biliary tract cancer has a high propensity for regional lymphatic metastases. An evidence-based nodal target definition of biliary tract cancer based on primary tumour location was proposed.