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Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p  = 5.6 × 10 –5 ). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

Local recurrence rates in operable rectal cancer are improved by radiotherapy (with or without chemotherapy) and surgical techniques such as total mesorectal excision. However, the contributions of surgery and radiotherapy to outcomes are unclear. We assessed the effect of the involvement of the circumferential resection margin and the plane of surgery achieved. In this prospective study, the plane of surgery achieved and the involvement of the circumferential resection margin were assessed by local pathologists, using a standard pathological protocol in 1156 patients with operable rectal cancer from the CR07 and NCIC-CTG CO16 trial, which compared short-course (5 days) preoperative radiotherapy and selective postoperative chemoradiotherapy, between March, 1998, and August, 2005. All analyses were by intention to treat. This trial is registered, number ISRCTN 28785842. 128 patients (11%) had involvement of the circumferential resection margin, and the plane of surgery achieved was classified as good (mesorectal) in 604 (52%), intermediate (intramesorectal) in 398 (34%), and poor (muscularis propria plane) in 154 (13%). We found that both a negative circumferential resection margin and a superior plane of surgery achieved were associated with low local recurrence rates. Hazard ratio (HR) was 0·32 (95% CI 0·16–0·63, p=0·0011) with 3-year local recurrence rates of 6% (5–8%) and 17% (10–26%) for patients who were negative and positive for circumferential resection margin, respectively. For plane of surgery achieved, HRs for mesorectal and intramesorectal groups compared with the muscularis propria group were 0·32 (0·16–0·64) and 0·48 (0·25–0·93), respectively. At 3 years, the estimated local recurrence rates were 4% (3–6%) for mesorectal, 7% (5–11%) for intramesorectal, and 13% (8–21%) for muscularis propria groups. The benefit of short-course preoperative radiotherapy did not differ in the three plane of surgery groups (p=0·30 for trend). Patients in the short-course preoperative radiotherapy group who had a resection in the mesorectal plane had a 3-year local recurrence rate of only 1%. In rectal cancer, the plane of surgery achieved is an important prognostic factor for local recurrence. Short-course preoperative radiotherapy reduced the rate of local recurrence for all three plane of surgery groups, almost abolishing local recurrence in short-course preoperative radiotherapy patients who had a resection in the mesorectal plane. The plane of surgery achieved should therefore be assessed and reported routinely. Medical Research Council (UK) and the National Cancer Institute of Canada.

Background Pre-operative chemoradiotherapy (CRT) for MRI-defined, locally advanced rectal cancer is primarily intended to reduce local recurrence rates by downstaging tumours, enabling an improved likelihood of curative resection. However, in a subset of patients complete tumour regression occurs implying that no viable tumour is present within the surgical specimen. This raises the possibility that surgery may have been avoided. It is also recognised that response to CRT is a key determinant of prognosis. Recent radiological advances enable this response to be assessed pre-operatively using the MRI tumour regression grade (mrTRG). Potentially, this allows modification of the baseline MRI-derived treatment strategy. Hence, in a ‘good’ mrTRG responder, with little or no evidence of tumour, surgery may be deferred. Conversely, a ‘poor response’ identifies an adverse prognostic group which may benefit from additional pre-operative therapy. Methods/design TRIGGER is a multicentre, open, interventional, randomised control feasibility study with an embedded phase III design. Patients with MRI-defined, locally advanced rectal adenocarcinoma deemed to require CRT will be eligible for recruitment. During CRT, patients will be randomised (1:2) between conventional management, according to baseline MRI, versus mrTRG-directed management. The primary endpoint of the feasibility phase is to assess the rate of patient recruitment and randomisation. Secondary endpoints include the rate of unit recruitment, acute drug toxicity, reproducibility of mrTRG reporting, surgical morbidity, pathological circumferential resection margin involvement, pathology regression grade, residual tumour cell density and surgical/specimen quality rates. The phase III trial will focus on long-term safety, regrowth rates, oncological survival analysis, quality of life and health economics analysis. Discussion The TRIGGER trial aims to determine whether patients with locally advanced rectal cancer can be recruited and subsequently randomised into a control trial that offers MRI-directed patient management according to radiological response to CRT (mrTRG). The feasibility study will inform a phase III trial design investigating stratified treatment of good and poor responders according to 3-year disease-free survival, colostomy-free survival as well as an increase in cases managed without a major resection. Trial registration ClinicalTrials.gov, ID: NCT02704520 . Registered on 5 February 2016.

BackgroundIt is widely believed that small rectal tumors are more likely to have a good response to neoadjuvant treatment, which may influence the selection of patients for a ‘watch and wait’ strategy.ObjectiveThe aim of this study was to investigate whether there is a relationship between baseline tumor length on magnetic resonance imaging (MRI) and response to chemoradiotherapy.MethodThe 96 patients with locally advanced rectal cancer randomised (2:1–intervention:control) in the TRIGGER feasibility study where eligible. Baseline tumor length was defined as the maximal cranio-caudal length on baseline MRI (mm) and was recorded prospectively at study registration. Magnetic resonance tumor regression grade (mrTRG) assessment was performed on the post-chemoradiotherapy (CRT) MRI 4–6 weeks (no later than 10 weeks) post completion of CRT. This was routinely reported for patients in the intervention (mrTRG-directed management) arm and reported for the purposes of this study by the central radiologist in the control arm patients. Those with an mrTRG I/II response were defined as ‘good responders’ and those with an mrTRG III–V response were defined as ‘poor responders’.ResultsOverall, 94 patients had a post-CRT MRI performed and were included. Forty-three (46%) patients had a good response (mrTRG I/II) and 51 (54%) patients had a poor response (mrTRG III/IV). The median tumor length of good responders was 43 mm versus 50 mm (p < 0.001), with considerable overlap in tumor lengths between groups.ConclusionBaseline tumor length on MRI is not a clinically useful biomarker to predict mrTRG tumor response to CRT and therefore patient suitability for a deferral of surgery trial.

Objectives To evaluate the potential of multi b -value DWI in predicting the prognosis of patients with locally advanced rectal cancer (LARC). Methods From 2015 to 2019, a total of 161 patients with LARC were enrolled and randomly sampled into a training set ( n = 113) and validation set ( n = 48). Multi b -value DWI ( b = 0~1500 s/mm 2 ) scans were postprocessed to generate functional parameters, including apparent diffusion coefficient (ADC), Dt, Dp, f , distributed diffusion coefficient (DDC), and α. Histogram features of each functional parameter were submitted into Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate COX analysis to generate DWI_score based on the training set. The prognostic model was constructed with functional parameter, DWI_score, and clinicopathologic factors by using univariate and multivariate COX analysis on the training set and verified on the validation set. Results Multivariate COX analysis revealed that DWI_score was an independent indicator for 5-year progression-free survival (PFS, HR = 5.573, p < 0.001), but not for overall survival (OS, HR = 2.177, p = 0.051). No mean value of functional parameters was correlated with PFS or OS. Prognostic model for 5-year PFS based on DWI_score, TNM-stage, mesorectal fascia (MRF), and extramural venous invasion (EMVI) showed good performance both in the training set (AUC = 0.819) and validation set (AUC = 0.815). Conclusions The DWI_score based on histogram features of multi b -value DWI functional parameters was an independent factor for PFS of LARC and the prognostic model with a combination of DWI_score and clinicopathologic factors could indicate the progression risk before treatment. Key Points • Mean value of functional parameters obtained from multi b-value DWI might not be useful to assess the prognosis of LARC. • The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent prognosis factor for PFS of LARC. • Prognostic model based on DWI_score and clinicopathologic factors could indicate the progression risk of LARC before treatment.

ObjectiveEndocuff Vision (ECV) is the second generation of a device designed to improve polyp detection. The aim of this study was to evaluate its impact on adenoma detection rate (ADR) in routine colonoscopy.DesignThis cluster-randomised crossover trial compared Endocuff-assisted (ECV+) with standard (ECV-) colonoscopy. Two teams of 11 endoscopists each with prior ECV experience, balanced in terms of basal ADR, gender and case volume were compared. In randomised fashion, the teams started with ECV+ or ECV- and switched group after inclusion of half of the cases. The main outcome criterion was ADR difference between ECV+ and ECV-. Subgroup analysis was done for physicians with low and high ADR (< or ≥ 25%).ResultsDuring two periods of 20 and 21 weeks, respectively, the 22 endoscopists included 2058 patients (1032 ECV- vs 1026 ECV+, both groups being comparable). Overall ADR for both groups taken together was higher with ECV (39.2%) than without (29.4%; p<0.001) irrespective of the sequence of use (ECV+ or ECV- first), but mostly in adenomas <1 cm. In the physician subgroup analysis, only high detectors showed a significant ADR increase (from 31% to 41%, p<0.001), while the increase in the low detectors was not significant (from 24% to 30%, p=0.11). ECV had a positive impact in all colonic locations, except for the rectum. No ECV- related complication was reported.ConclusionWe observed a significant ADR difference of approximately 10% by the use of ECV. By subgroup analysis, this increase was significant only in physicians classified as high detectors.Trial registration numberClinicalTrials.gov (NCT03344055).

Background Microsatellite instability (MSI) predetermines responses to adjuvant 5‐fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning model that could preoperatively predict the MSI status of rectal cancer based on magnetic resonance images. Methods This single‐center retrospective study included 491 rectal cancer patients with pathologically proven microsatellite status. Patients were randomly divided into the training/validation cohort (n = 395) and the testing cohort (n = 96). A clinical model using logistic regression was constructed to discriminate MSI status using only clinical factors. Based on a modified MobileNetV2 architecture, deep learning models were tested for the predictive ability of MSI status from magnetic resonance images, with or without integrating clinical factors. Results The clinical model correctly classified 37.5% of MSI status in the testing cohort, with an AUC value of 0.573 (95% confidence interval [CI], 0.468 ~ 0.674). The pure imaging‐based model and the combined model correctly classified 75.0% and 85.4% of MSI status in the testing cohort, with AUC values of 0.820 (95% CI, 0.718 ~ 0.884) and 0.868 (95% CI, 0.784 ~ 0.929), respectively. Both deep learning models performed better than the clinical model (p < 0.05). There was no statistically significant difference between the deep learning models with or without integrating clinical factors. Conclusions Deep learning based on high‐resolution T2‐weighted magnetic resonance images showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients. Microsatellite instability (MSI) serves as a prognostic biomarker for clinical outcomes. We developed and validated a deep learning model that could preoperatively predict the MSI status of rectal cancer based on MR and found deep learning based on MRI showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients.

BackgroundIn the field of rectal cancer surgery, there remains ongoing debate on the merits of high ligation (HL) and low ligation (LL) of the inferior mesenteric artery (IMA) in terms of perfusion and anastomosis leakage. Recently, infrared fluorescence of indocyanine green (ICG) imaging has been used to evaluate perfusion status during colorectal surgery.ObjectiveThe purpose of this study is to compare the changes in perfusion status between HL and LL through quantitative evaluation of ICG.MethodsPatients with rectosigmoid or rectal cancer were randomized into a high or LL group. ICG was injected before and after IMA ligation, and region of interest (ROI) values were measured by an image analysis program (HSL video©).ResultsFrom February to July 2020, 22 patients were enrolled, and 11 patients were assigned to each group. Basic demographics were similar between the two groups, except for albumin level and cardiac ejection fraction. There were no significant differences in F_max between the two groups, but T_max was significantly higher and Slope_max was significantly lower in the HL group than in the LL group. Anastomosis leakage was significantly associated with neoadjuvant chemoradiation and F_max.ConclusionAfter IMA ligation, T_max increased and Slope_max decreased significantly in the HL group. However, the intensity of perfusion status (F_max) did not change according to the level of IMA ligation.

ObjectivesTo investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer.MethodsThis was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T2-weighted images, diffusion-weighted images and enhanced T1-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong’s test, receiver operating characteristic, calibration and decision curve analysis to assess the models’ performance.ResultsThe three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong’s test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong’s test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice.ConclusionA nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.

Background Mutated KRAS may indicate an invasive nature and predict prognosis in locally advanced rectal cancer (LARC). We aimed to establish a radiomic model using pretreatment T2W MRIs to predict KRAS status and explore the association between the KRAS status or model predictions and lung metastasis. Methods In this retrospective multicentre study, LARC patients from two institutions between January 2012 and January 2019 were randomly divided into training and testing cohorts. Least absolute shrinkage and selection operator (LASSO) regression and the support vector machine (SVM) classifier were utilized to select significant radiomic features and establish a prediction model, which was validated by radiomic score distribution and decision curve analysis. The association between the model stratification and lung metastasis was investigated by Cox regression and Kaplan‒Meier survival analysis; the results were compared by the log-rank test. Results Overall, 103 patients were enrolled (73 and 30 in the training and testing cohorts, respectively). The median follow-up was 38.1 months (interquartile range: 26.9, 49.4). The radiomic model had an area under the curve (AUC) of 0.983 in the training cohort and 0.814 in the testing cohort. Using a cut-off of 0.679 defined by the receiver operating characteristic (ROC) curve, patients with a high radiomic score (RS) had a higher risk for lung metastasis (HR 3.565, 95% CI 1.337, 9.505, p  = 0.011), showing similar predictive performances for the mutant and wild-type KRAS groups (HR 3.225, 95% CI 1.249, 8.323, p  = 0.016, IDI: 1.08%, p  = 0.687; NRI 2.23%, p  = 0.766). Conclusions We established and validated a radiomic model for predicting KRAS status in LARC. Patients with high RS experienced more lung metastases. The model could noninvasively detect KRAS status and may help individualize clinical decision-making.