Search

Neoadjuvant programmed cell death protein 1 (PD‐1) blockade exhibits promising efficacy in patients with mismatch repair deficient (dMMR) colorectal cancer (CRC). However, discrepancies between radiological and histological findings have been reported in the PICC phase II trial (NCT 03926338). Therefore, we strived to discern radiological features associated with pathological complete response (pCR) based on computed tomography (CT) images. Data were obtained from the PICC trial that included 36 tumors from 34 locally advanced dMMR CRC patients, who received neoadjuvant PD‐1 blockade for 3 months. Among the 36 tumors, 28 (77.8%) tumors achieved pCR. There were no statistically significant differences in tumor longitudinal diameter, the percentage change in tumor longitudinal diameter from baseline, primary tumor sidedness, clinical stage, extramural venous invasion status, intratumoral calcification, peritumoral fat infiltration, intestinal fistula and tumor necrosis between the pCR and non‐pCR tumors. Otherwise, tumors with pCR had smaller posttreatment tumor maximum thickness (median: 10 mm vs 13 mm, P =.004) and higher percentage decrease in tumor maximum thickness from baseline (52.9% vs 21.6%, P =.005) compared to non‐pCR tumors. Additionally, a higher proportion of the absence of vascular sign (P =.003, odds ratio [OR] = 25.870 [95% CI, 1.357‐493.110]), nodular sign (P <.001, OR = 189.000 [95% CI, 10.464‐3413.803]) and extramural enhancement sign (P =.003, OR = 21.667 [2.848‐164.830]) was observed in tumors with pCR. In conclusion, these CT‐defined radiological features may have the potential to serve as valuable tools for clinicians in identifying patients who have achieved pCR after neoadjuvant PD‐1 blockade, particularly in individuals who are willing to adopt a watch‐and‐wait strategy. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to build a SVM classifier using ResNet-3D algorithm by artificial intelligence for prediction of synchronous PC. Background: Adequate detection and staging of PC from CRC remain difficult. Methods: The primary tumors in synchronous PC were delineated on preoperative contrast-enhanced computed tomography (CT) images. The features of adjacent peritoneum were extracted to build a ResNet3D + SVM classifier. The performance of ResNet3D + SVM classifier was evaluated in the test set and was compared to routine CT which was evaluated by radiologists. Results: The training set consisted of 19,814 images from 54 patients with PC and 76 patients without PC. The test set consisted of 7837 images from 40 test patients. The ResNet-3D spent only 34 seconds to analyze the test images. To increase the accuracy of PC detection, we have built a SVM classifier by integrating ResNet-3D features with twelve PC-specific features (P < 0.05). The ResNet3D + SVM classifier showed accuracy of 94.11% with AUC of 0.922 (0.912-0.944), sensitivity of 93.75%, specificity of 94.44%, positive predictive value (PPV) of 93.75%, and negative predictive value (NPV) of 94.44% in the test set. The performance was superior to routine contrast-enhanced CT (AUC: 0.791). Conclusions: The ResNet3D + SVM classifier based on deep learning algorithm using ResNet-3D framework has shown great potential in prediction of synchronous PC in CRC. [ABSTRACT FROM AUTHOR]

Background External rectal prolapse is a relatively rare disease, in which male patients account for a minority. The selection of abdominal repair or perineal repair for male patients has rarely been investigated. Methods Fifty-one male patients receiving abdominal repair (laparoscopic ventral rectopexy) or perineal repair (Delorme or Altemeier procedures) at the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) between March 2013 and September 2019 were retrospectively analysed. We compared the recurrence, complication rate, post-operative defecation disorder, length of stay, and quality of life between the abdominal and perineal groups. Results Of the 51 patients, 45 had a complete follow-up, with a median of 48.5 months (range, 22.8–101.8 months). A total of 35 patients were under age 40 years. The complication rate associated with abdominal repair was less than that associated with perineal repair (0% vs 20.7%, P  = 0.031) and the recurrence rate was also lower (9.5% vs 41.7%, P   =  0.018). Multivariate analysis showed that perineal repair (odds ratio, 9.827; 95% confidence interval, 1.296–74.50; P  = 0.027) might be a risk factor for recurrence. Moreover, only perineal repair significantly improved post-operative constipation status (preoperative vs post-operative, 72.4% vs 25.0%, P   =  0.001). There was no reported mortality in either of the groups. No patient's sexual function was affected by the surgery. Conclusions Both surgical approaches were safe in men. Compared with perineal repair, the complication rate and recurrence rate for abdominal repair were lower. However, perineal repair was better able to correct constipation. [ABSTRACT FROM AUTHOR]

Purpose: The role of neoadjuvant chemotherapy (NAC) in colon cancer remains unclear. This trial investigated whether 3 months of modified infusional fluorouracil, leucovorin, and oxaliplatin (mFOLFOX6) or capecitabine and oxaliplatin (CAPOX) as NAC could improve outcomes in patients with locally advanced colon cancer versus upfront surgery., Patients and Methods: OPTICAL was a randomized, phase III trial in patients with clinically staged locally advanced colon cancer (T3 with extramural spread into the mesocolic fat ≥5 mm or T4). Patients were randomly assigned 1:1 to receive six preoperative cycles of mFOLFOX6 or four cycles of CAPOX, followed by surgery and adjuvant chemotherapy (NAC group), or immediate surgery and the physician's choice of adjuvant chemotherapy (upfront surgery group). The primary end point was 3-year disease-free survival (DFS) assessed in the modified intention-to-treat (mITT) population., Results: Between January 2016 and April 2021, of the 752 patients enrolled, 744 patients were included in the mITT analysis (371 in the NAC group; 373 in the upfront surgery group). At a median follow-up of 48.0 months (IQR, 46.0-50.1), 3-year DFS rates were 82.1% in the NAC group and 77.5% in the upfront surgery group (stratified hazard ratio [HR], 0.74 [95% CI, 0.54 to 1.03]). The R0 resection was achieved in 98% of patients who underwent surgery in both groups. Compared with upfront surgery, NAC resulted in a 7% pathologic complete response rate (pCR), significantly lower rates of advanced tumor staging (pT3-4: 77% v 94%), lymph node metastasis (pN1-2: 31% v 46%), and potentially improved overall survival (stratified HR, 0.44 [95% CI, 0.25 to 0.77])., Conclusion: NAC with mFOLFOX6 or CAPOX did not show a significant DFS benefit. However, this neoadjuvant approach was safe, resulted in substantial pathologic downstaging, and appears to be a viable therapeutic option for locally advanced colon cancer.

Background: According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing. However, the routine MR sequences for patients with IDD are T1- and T2-MR, T1ρ-MR is not commonly used due to long scanning time and extra expenses, which limits the application of T1ρ-MR based IDD phases., Purpose: To build a deep learning model to achieve the classification of T1ρ-MR based IDD phases from routine T1-MR images., Study Type: Retrospective., Population: Sixty (M/F: 35/25) patients with low back pain or lower limb radiculopathy are randomly divided into training (N = 50) and test (N = 10) sets., Field Strength/sequences: 1.5 T MR scanner; T1-, T2-, and T1ρ-MR sequence (spin echo)., Assessment: The T1ρ values of the nucleus pulposus in intervertebral discs (IVDs) were measured. IVDs were divided into three phases based on the mean T1ρ value: pre-degeneration phase (mean T1ρ value >110 msec), rapid degeneration phase (mean T1ρ value: 80-110 msec), and late degeneration phase (mean T1ρ value <80 msec). After measurement, the T1ρ values, phases, and levels of IVDs were input into the model as labels., Statistical Tests: Intraclass correlation coefficient, area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall (P < 0.05 was considered significant)., Results: In the test dataset, the model achieved a mean average precision of 0.996 for detecting IVD levels. The diagnostic accuracy of the T1ρ-MR based IDD phases was 0.840 and the AUC was 0.871, the average AUC of 5-folds cross validation was 0.843., Data Conclusion: The proposed deep learning model achieved the classification of T1ρ-MR based IDD phases from routine T1-MR images, which may provide a method to facilitate the application of T1ρ-MR in IDD., Evidence Level: 4 TECHNICAL EFFICACY: Stage 2., (© 2024 The Author(s). Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Background: Neoadjuvant chemotherapy (NCT) alone can achieve comparable treatment outcomes to chemoradiotherapy in locally advanced rectal cancer (LARC) patients. This study aimed to investigate the value of texture analysis (TA) in apparent diffusion coefficient (ADC) maps for identifying non-responders to NCT., Methods: This retrospective study included patients with LARC after NCT, and they were categorized into nonresponse group (pTRG 3) and response group (pTRG 0-2) based on pathological tumor regression grade (pTRG). Predictive texture features were extracted from pre- and post-treatment ADC maps to construct a TA model using RandomForest. The ADC model was developed by manually measuring pre- and post-treatment ADC values and calculating their changes. Simultaneously, subjective evaluations based on magnetic resonance imaging assessment of TRG were performed by two experienced radiologists. Model performance was compared using the area under the curve (AUC) and DeLong test., Results: A total of 299 patients from two centers were divided into three cohorts: the primary cohort (center A; n  =   194, with 36 non-responders and 158 responders), the internal validation cohort (center A; n  =   49, with 9 non-responders) and external validation cohort (center B; n  =   56, with 33 non-responders). The TA model was constructed by post&#95;mean, mean&#95;change, post&#95;skewness, post&#95;entropy, and entropy&#95;change, which outperformed both the ADC model and subjective evaluations with an impressive AUC of 0.997 (95% confidence interval [CI], 0.975-1.000) in the primary cohort. Robust performances were observed in internal and external validation cohorts, with AUCs of 0.919 (95% CI, 0.805-0.978) and 0.938 (95% CI, 0.840-0.985), respectively., Conclusions: The TA model has the potential to serve as an imaging biomarker for identifying nonresponse to NCT in LARC patients, providing a valuable reference for these patients considering additional radiation therapy., Competing Interests: The authors declare that there is no conflict of interests in this study., (© The Author(s) 2024. Published by Oxford University Press and Sixth Affiliated Hospital of Sun Yat-sen University.)

Deep learning facilitates complex medical data analysis and is increasingly being explored in colorectal cancer diagnostics. However, the training cost of the deep learning model limits its real-world medical utility. In this study, we present a composite network that combines deep learning and unsupervised K-means clustering algorithm (RK-net) for automatic processing of medical images. RK-net was more efficient in image refinement compared with manual screening and annotation. The training of a deep learning model for colorectal cancer diagnosis was accelerated by two times with utilization of RK-net-processed images. Better performance was observed in training loss and accuracy achievement as well. RK-net could be useful to refine medical images of the ever-expanding quantity and assist in subsequent construction of the artificial intelligence model.

Objective: To forward the magnetic resonance imaging (MRI) based distance between the deepest tumor invasion and mesorectal fascia (DMRF), and to explore its prognosis differentiation value in cT3 stage rectal cancer with comparison of cT3 substage., Methods: This was a retrospective, multicenter cohort study including cT3 rectal cancer patients undergoing neoadjuvant chemoradiotherapy followed by radical surgery from January 2013 to September 2014. DMRF and cT3 substage were evaluated from baseline MRI. The cutoff of DMRF was determined by disease progression. Multivariate cox regression was used to test the prognostic values of baseline variables., Results: A total of 804 patients were included, of which 226 (28.1%) developed progression. A DMRF cutoff of 7 mm was chosen. DMRF category, the clock position of the deepest position of tumor invasion (CDTI) and extramural venous invasion (EMVI) were independent predictors for disease progression, and hazard ratios (HRs) were 0.26 [95% confidence interval (95% CI), 0.13-0.56], 1.88 (95% CI, 1.33-2.65) and 1.57 (95% CI, 1.13-2.18), respectively. cT3 substage was not a predictor for disease progression., Conclusions: The measurement of DMRF value on baseline MRI can better distinguish cT3 rectal cancer prognosis rather than cT3 substage, and was recommended in clinical evaluation., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare., (Copyright ©2021Chinese Journal of Cancer Research. All rights reserved.)