PURPOSE: To ascertain if preoperative short-term radiotherapy followed by chemotherapy is not inferior to a standard schedule of long-term chemoradiotherapy in patients with locally advanced rectal cancer. MATERIALS AND METHODS: Patients with distal or middle-third, clinical primary tumor stage 3-4 and/or regional lymph node–positive rectal cancer were randomly assigned (1:1) to short-term radiotherapy (25 Gy in five fractions over 1 week) followed by four cycles of chemotherapy (total neoadjuvant therapy [TNT]) or chemoradiotherapy (50 Gy in 25 fractions over 5 weeks, concurrently with capecitabine [chemoradiotherapy; CRT]). Total mesorectal excision was undertaken 6-8 weeks after preoperative treatment, with two additional cycles of CAPOX (intravenous oxaliplatin [130 mg/m², once a day] on day 1 and capecitabine [1,000 mg/m², twice a day] from days 1 to 14) in the TNT group and six cycles of CAPOX in the CRT group. The primary end point was 3-year disease-free survival (DFS). RESULTS: Between August 2015 and August 2018, a total of 599 patients were randomly assigned to receive TNT (n = 302) or CRT (n = 297). At a median follow-up of 35.0 months, 3-year DFS was 64.5% and 62.3% in TNT and CRT groups, respectively (hazard ratio, 0.883; one-sided 95% CI, not applicable to 1.11; P < .001 for noninferiority). There was no significant difference in metastasis-free survival or locoregional recurrence, but the TNT group had better 3-year overall survival than the CRT group (86.5% v 75.1%; P = .033). Treatment effects on DFS and overall survival were similar regardless of prognostic factors. The prevalence of acute grade III-V toxicities during preoperative treatment was 26.5% in the TNT group versus 12.6% in the CRT group ( P < .001). CONCLUSION: Short-term radiotherapy with preoperative chemotherapy followed by surgery was efficacious with acceptable toxicity and could be used as an alternative to CRT for locally advanced rectal cancer.

Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly designed) and deep learning (DL)-based radiomic features extracted from pre-treatment diffusion-weighted magnetic resonance images (DWIs) for predicting neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC). 43 Patients receiving nCRT were included. All patients underwent DWIs before nCRT and total mesorectal excision surgery 6–12 weeks after completion of nCRT. Gross tumor volume (GTV) contours were drawn by an experienced radiation oncologist on DWIs. The patient-cohort was split into the responder group ( n = 22) and the non-responder group ( n = 21) based on the post-nCRT response assessed by postoperative pathology, MRI or colonoscopy. Handcrafted and DL-based features were extracted from the apparent diffusion coefficient (ADC) map of the DWI using conventional computer-aided diagnosis methods and a pre-trained convolution neural network, respectively. Least absolute shrinkage and selection operator (LASSO)-logistic regression models were constructed using extracted features for predicting treatment response. The model performance was evaluated with repeated 20 times stratified 4-fold cross-validation using receiver operating characteristic (ROC) curves and compared using the corrected paired t -test. The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0.64, while the one built with DL-based features yielded the mean AUC of 0.73. The corrected paired t -test on AUC showed P -value < 0.05. DL-based features extracted from pre-treatment DWIs achieved significantly better classification performance compared with handcrafted features for predicting nCRT response in patients with LARC.

Summary Cancer immunotherapy has revolutionized tumor treatment. However, robust and effective testing platforms remain lacking, especially for the selection of the optimized therapy at the patient-specific level. Unlike conventional treatment evaluations, testing platforms for cancer immunotherapy must incorporate not only tumor cells but also the tumor microenvironment (TME), including immune components. Recently, emergence of patient-derived tumor organoids (PDTOs), an in vitro preclinical model, has provided a novel approach for studying tumor evolution and assessing treatment responses, and shows great potential when coculturing with immune cells to study the mechanisms of immunotherapy efficacy and resistance. However, traditional organoid technology is limited in capturing the full impact of the TME on tumor behaviors due to the absence of stromal components. To circumvent these restrictions, complex organoid cocultures with immune cells, cancer-associated fibroblasts and vasculatures are developed. In this review, we summarized recent advances in PDTO culture techniques for modeling the TME and explored the application of complex tumor organoids in cancer immunotherapy.

Purpose: To create a prognostic prediction radiomics model for soft tissue sarcoma (STS) of the extremities and trunk treated with neoadjuvant radiotherapy. Methods: This study included 62 patients with STS of the extremities and trunk who underwent magnetic resonance imaging (MRI) before neoadjuvant radiotherapy. After tumour segmentation and preprocessing, 851 radiomics features were extracted. The radiomics score was constructed according to the least absolute shrinkage and selection operator (LASSO) method. Survival analysis (disease-free survival; DFS) was performed using the log-rank test and Cox’s proportional hazards regression model. The nomogram model was established based on the log-rank test and Cox regression model. Harrell’s concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic factors. The clinical utility of the model was assessed by decision curve analysis (DCA). Results: The univariate survival analysis showed that tumour location (p = 0.032), clinical stage (p = 0.022), tumour size (p = 0.005) and the radiomics score were correlated with DFS (p < 0.05). The multivariate analysis showed that tumour location, tumour size, and the radiomics score were independent prognostic factors for DFS (p < 0.05). The combined clinical-radiomics model based on the multivariate analysis showed the best predictive ability for DFS (C-index: 0.781; Area Under Curve: 0.791). DCA revealed that the use of the radiomics score-based nomogram was associated with better benefit gains relative to the prediction of 2-year DFS events than other models in the threshold probability range between 0.12 and 0.38. Conclusion: The radiomics score from pretreatment MRI is an independent prognostic factor for DFS in patients with STS of the extremities and trunk. The radiomics score-based nomogram could improve prognostic stratification ability and thus contribute to individualized therapy for STS patients.

Objective: This meta-analysis evaluated the efficacy and safety of accelerated partial breast irradiation versus whole-breast irradiation for early-stage breast cancer after breast-conserving surgery. Materials and methods: A systematic search of PubMed, Embase, and the Cochrane libraries was performed according to the PRISMA statement the last 10 years to April 7, 2020 to identify the randomized controlled trials of APBI versus WBI for treating patients with early-stage breast cancer. Two independent observers evaluated the identified studies. The obtained data were analyzed using the RevMan 5.3 software. Results: A total of 10 randomized controlled trials involving 15,500 patients with early-stage breast cancer were selected according to the inclusion and exclusion criteria and included in this meta-analysis. In this meta-analysis, we included ten studies that reported local recurrence and found significant differences in local recurrence rates (HR = 1.46; 95% CI 1.20–1.79, P = 0.0002). Further analysis showed that this difference may be related to the choice of treatment methods. No differences in distant metastasis, breast cancer deaths, contralateral breast cancer, disease-free survival, and overall survival rates were observed between WBI and APBI groups. There was no significant difference in late toxicity, cosmetic outcomes and quality of life between the two groups, the compliance and tolerance of the patients were well. Compared to whole breast irradiation, accelerated partial breast irradiation significantly reduced serious (≥ grade 2) early toxicities, especially regarding acute skin toxicity. Conclusions: The analysis showed that patients receiving APBI had a higher local recurrence rate, but no differences in distant metastasis, breast cancer deaths, contralateral breast cancer, disease-free survival, and overall survival rates.

Radiation pneumonitis (RP) is one of the common side effects after adjuvant radiotherapy in breast cancer. Irradiation dose to normal lung was related to RP. We aimed to propose an organ features based on deep learning (DL) model and to evaluate the correlation between normal lung dose and organ features. Patients with pathology-confirmed invasive breast cancer treated with adjuvant radiotherapy following breast-conserving surgery in four centers were included. From 2019 to 2020, a total of 230 patients from four nationwide centers in China were screened, of whom 208 were enrolled for DL modeling, and 22 patients from another three centers formed the external testing cohort. The subset of the internal testing cohort (n = 42) formed the internal correlation testing cohort for correlation analysis. The outline of the ipsilateral breast was marked with a lead wire before the scanning. Then, a DL model based on the High-Resolution Net was developed to detect the lead wire marker in each slice of the CT images automatically, and an in-house model was applied to segment the ipsilateral lung region. The mean and standard deviation of the distance error, the average precision, and average recall were used to measure the performance of the lead wire marker detection model. Based on these DL model results, we proposed an organ feature, and the Pearson correlation coefficient was calculated between the proposed organ feature and ipsilateral lung volume receiving 20 Gray (Gy) or more (V20). For the lead wire marker detection model, the mean and standard deviation of the distance error, AP (5 mm) and AR (5 mm) reached 3.415 ± 4.529, 0.860, 0.883, and 4.189 ± 8.390, 0.848, 0.830 in the internal testing cohort and external testing cohort, respectively. The proposed organ feature calculated from the detected marker correlated with ipsilateral lung V20 (Pearson correlation coefficient, 0.542 with p < 0.001 in the internal correlation testing cohort and 0.554 with p = 0.008 in the external testing cohort). The proposed artificial Intelligence-based CT organ feature was correlated with normal lung dose in adjuvant radiotherapy following breast-conserving surgery in patients with invasive breast cancer. NCT05609058 (08/11/2022).