ABSTRACT
Objective: To examine the radiomics model based on high-resolution T2WI and diffusion weighted imaging (DWI) in predicting microsatellite stability in patients with stage Ⅱ and Ⅲ rectal cancer. Methods: From February 2016 to October 2020, 175 patients with stage Ⅱ and Ⅲ rectal cancer who met the inclusion criteria were retrospectively collected. There were 119 males and 56 females, aged (63.9±9.4) years (range: 37 to 85 years), including 152 patients with microsatellite stability and 23 patients with microsatellite instability. All patients were randomly divided into the training group (n=123) and the validation group (n=52) with a ratio of 7∶3. The region of interest was labeled on the T2WI and DWI images of each patient using the ITK-SNAP software, and PyRadiomics was used to extract seven kinds of radiomics features. After removing redundant features and normalizing features, the least absolute shrinkage and selection operation were used for feature selection. One clinical model, three radiomics models and one clinical-radiomics model were constructed in the training group based on a support vector machine. The area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were used to evaluate the performance of the models in the verification group. Results: Three clinical features (age, degree of tumor differentiation, and distance from the lower edge of the tumor to the anal edge) and six radiomics features (two DWI-related features and four T2WI-related features) most related to microsatellite status of rectal cancer patients were selected. The AUC of the clinical-radiomics model in the training group was 0.95. In the validation group, the AUC was 0.81, better than the clinical model (0.68, Z=0.71, P=0.04), and equivalent to the T2WI+DWI model (0.82, Z=0.21, P=0.83). Conclusions: Radiomic features based on preoperative T2WI and DWI were related to microsatellite stability in patients with stage Ⅱ and Ⅲ rectal cancer and showed a high classification efficiency. The model based on the features provided a noninvasive and convenient tool for preoperative determination of microsatellite stability in rectal cancer patients.
ABSTRACT
Objective: To compare the short-term and long-term outcomes between robotic-assisted and laparoscopic-assisted radical right hemicolectomy in patients with adenocarcinoma of the right colon. Methods: Retrospective review of a prospectively collected database identified 288 right colon cancer patients who underwent either robotic-assisted (n=57) or laparoscopic-assisted right hemicolectomy (n=231) between October 2014 and October 2020 at Department of Gastrointestinal Surgery, the Affiliated Hospital of Qingdao University. There were 161 males and 127 females, aging (60.3±12.8) years (range: 17 to 86 years). After propensity score matching as 1∶4 between robotic-assisted and laparoscopic-assisted right hemicolectomy, there were 56 cases in robotic group and 176 cases in laparoscipic group. Perioperative outcomes and overall survival were compared between the two groups using t test, Wilcoxon rank sum test, χ2 test, Fisher exact test, Kaplan-Meier method and Log-rank test, respectively. Results: The total operative time was similar between the robotic and laparoscopic group ((206.9±60.7) minutes vs. (219.9±56.3) minutes, t=-1.477, P=0.141). Intraoperative bleeding was less in the robotic group (50 (20) ml vs. 50 (50) ml, Z=-4.591, P<0.01), while the number of lymph nodes retrieved was significantly higher (36.0±10.0 vs. 29.0±10.1, t=4.491, P<0.01). Patients in robotic group experienced significantly shorter hospital stay, shorter time to first flatus, and defecation (t: -2.888, -2.946, -2.328, all P<0.05). Moreover, the overall peri-operative complication rate was similar between robotic and laparoscopic group (17.9% vs. 22.7%, χ²=0.596,P=0.465). The 3-year overall survival were 92.9% and 87.9% respectively and the 3-year disease-free survival rates were 83.1% and 82.6% with no statistical significance between the robotic and laparoscopic group (P>0.05). Conclusions: Compared to laparoscopic-assisted right hemicolectomy, robot-assisted right hemicolectomy could improve some short-term clinical outcomes. The two procedures are both achieving comparable survival.
Subject(s)
Female , Humans , Male , Colectomy , Colonic Neoplasms/surgery , Laparoscopy , Prognosis , Propensity Score , Retrospective Studies , Robotic Surgical Procedures , Treatment OutcomeABSTRACT
BACKGROUND@#Colorectal cancer is harmful to the patient's life. The treatment of patients is determined by accurate preoperative staging. Magnetic resonance imaging (MRI) played an important role in the preoperative examination of patients with rectal cancer, and artificial intelligence (AI) in the learning of images made significant achievements in recent years. Introducing AI into MRI recognition, a stable platform for image recognition and judgment can be established in a short period. This study aimed to establish an automatic diagnostic platform for predicting preoperative T staging of rectal cancer through a deep neural network.@*METHODS@#A total of 183 rectal cancer patients' data were collected retrospectively as research objects. Faster region-based convolutional neural networks (Faster R-CNN) were used to build the platform. And the platform was evaluated according to the receiver operating characteristic (ROC) curve.@*RESULTS@#An automatic diagnosis platform for T staging of rectal cancer was established through the study of MRI. The areas under the ROC curve (AUC) were 0.99 in the horizontal plane, 0.97 in the sagittal plane, and 0.98 in the coronal plane. In the horizontal plane, the AUC of T1 stage was 1, AUC of T2 stage was 1, AUC of T3 stage was 1, AUC of T4 stage was 1. In the coronal plane, AUC of T1 stage was 0.96, AUC of T2 stage was 0.97, AUC of T3 stage was 0.97, AUC of T4 stage was 0.97. In the sagittal plane, AUC of T1 stage was 0.95, AUC of T2 stage was 0.99, AUC of T3 stage was 0.96, and AUC of T4 stage was 1.00.@*CONCLUSION@#Faster R-CNN AI might be an effective and objective method to build the platform for predicting rectal cancer T-staging.@*TRIAL REGISTRATION@#chictr.org.cn: ChiCTR1900023575; http://www.chictr.org.cn/showproj.aspx?proj=39665.
Subject(s)
Humans , Artificial Intelligence , Magnetic Resonance Imaging , Neoplasm Staging , Neural Networks, Computer , Rectal Neoplasms/pathology , Retrospective StudiesABSTRACT
BACKGROUND@#Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer. This study was performed to develop an automatic and accurate imaging processing technique system, allowing this system to read computed tomography (CT) images correctly and make diagnosis of pancreatic cancer faster.@*METHODS@#The establishment of the artificial intelligence (AI) system for pancreatic cancer diagnosis based on sequential contrast-enhanced CT images were composed of two processes: training and verification. During training process, our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set. Additionally, we used VGG16, which was pre-trained in ImageNet and contained 13 convolutional layers and three fully connected layers, to initialize the feature extraction network. In the verification experiment, we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network (Faster R-CNN) model that had completed training. Totally, 1699 images from 100 pancreatic cancer patients were included for clinical verification.@*RESULTS@#A total of 338 patients with pancreatic cancer were included in the study. The clinical characteristics (sex, age, tumor location, differentiation grade, and tumor-node-metastasis stage) between the two training and verification groups were insignificant. The mean average precision was 0.7664, indicating a good training effect of the Faster R-CNN. Sequential contrast-enhanced CT images of 100 pancreatic cancer patients were used for clinical verification. The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632. It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image, which is much faster than the time required for diagnosis by an imaging specialist.@*CONCLUSIONS@#Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.@*TRIAL REGISTRATION@#ChiCTR1800017542; http://www.chictr.org.cn.
ABSTRACT
<p><b>BACKGROUND</b>Vitamin D status in relation to pancreatic cancer risks is still inconsistent. This study was performed to evaluate the association between vitamin D status and risk of pancreatic cancer using a meta-analysis approach.</p><p><b>METHODS</b>A systemic review of all relevant literature in English was performed by searching Pubmed, Web of Science and Embase to identify eligible studies from the earliest available date to April 1, 2012. The search terms "vitamin D", "25-hydroxyvitamin D", "pancreatic cancer" or "pancreatic neoplasms" were used to retrieve relevant papers. Inclusion criteria were: (1) the exposure of interest was intake of vitamin D or blood levels of vitamin D; (2) the outcome of interest was pancreatic cancer; (3) data on high and low intake or blood vitamin D in cases and controls were available; (4) odds ratio (OR) estimates with 95% confidence interval (CI) were provided; (5) primary epidemiological data were provided reporting pancreatic cancer incidence. The combined OR values and their 95% CIs were calculated via a meta-analysis. The potential presence of publication bias was estimated using Egger's regression asymmetry test.</p><p><b>RESULTS</b>Nine studies with a total of 1 206 011 participants met the inclusion criteria. The test for heterogeneity showed there were significant differences among the included studies (I(2)=70.9%, P=0.001), so a randomized-effects model was used in the meta-analysis. The pooled OR of pancreatic cancer for the highest versus the lowest categories of vitamin D level was 1.14 (95% CI 0.896-1.451), and the Z-score for the overall effect was 1.06 (P=0.288), showing that there was no significant association between vitamin D levels and the risk of pancreatic cancer. Egger's test indicated there was a low possibility of publication bias in this study (P=0.348).</p><p><b>CONCLUSION</b>Dietary vitamin D or circulating concentrations of 25-hydroxyvitamin D are not associated with the risk of pancreatic cancer based on evidence from currently published studies.</p>