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Classification of the colonic splenic flexure based on three-dimensional CT analysis.

ABSTRACT: Mobilization of the splenic flexure can be a challenging surgical step in colorectal surgery. This study aimed to classify the splenic flexure based on the three-dimensional (3D) coordinates of the splenic hilum and left renal hilum. This classification was used to compare splenic flexure mobilization during colorectal resection. CT images of patients with colorectal cancer treated between April 2018 and December 2019 were analysed retrospectively. 3D mutual positioning of the splenic flexure from the ligament of Treitz to the splenic hilum or the left renal hilum was used to classify patients into three groups using cluster analysis. The difference in the procedure time between groups was also analysed in a subset of patients undergoing laparoscopic colectomy with complete splenic flexure mobilization. Of 515 patients reviewed, 319 with colorectal cancers were included in the study and categorized based on the 3D coordinates of the splenic hilum and left renal hilum as caudal (100 patients), cranial (118) and lateral (101) positions. Male sex (P < 0.001), older age (P = 0.004) and increased bodyweight (P = 0.043) were independent characteristics of the lateral group in multiple logistic regression analysis. Thirty-four patients underwent complete splenic flexure mobilization during the study period; this took significantly longer (mean 78.7 min) in the lateral group than in the caudal and cranial groups (41.8 and 43.2 min respectively; P = 0.006). Locating the splenic flexure using 3D coordinates could be helpful in predicting a longer duration for mobilization of the splenic flexure.

SUBMITTER: Kawai K

PROVIDER: S-EPMC8271130 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPrevious classification of renal pelvicalyceal anatomical structure may be difficult to intuitively understand and unpractical for endourological surgery. We aim to put forward a modified Takazawa anatomical classification of renal pelvicalyceal system based on three-dimensional (3D) virtual reconstruction models for endourological surgery.MethodsWe retrospectively collected data on 225 patients (320 kidneys) in total between Apr. 2017 and Dec. 2020, spatial anatomical structure of renal pelvis and calyces were modeled and corresponding morphological parameters were measured after 3D virtual reconstruction of computed tomography urography (CTU). The modified Takazawa renal pelvicalyceal anatomical classification was advanced based on the renal pelvicalyceal morphological parameters [bifurcated branches of renal pelvis, cross sectional area of renal pelvis and ureteropelvic junction (UPJ), infundibuloureteral angle (IUA), lower pole infundibular calyceal length (IL)] by 3D virtual reconstruction models, and comparison of renal pelvicalyceal system morphological parameters were performed to evaluate the differences in various classification types of renal pelvis and calyces.ResultsAnatomical structure of renal pelvis and calyces were divided into two main types (Type A and Type B) according to renal pelvic branch patterns. A single pelvis without bifurcated branch was regarded as Type A (62%) and subclassified into three subtypes: Type A1 (22%), Type A2 (27%) and Type A3 (13%), the slimline pelvis was classified as Type A1, the typical pelvis as Type A2 and the broad pelvis as Type A3. A divided pelvis with bifurcated branches was seen as Type B (38%) and subclassified into two subtypes: Type B1 (15%) with the wide and flat lower calyx branch, Type B2 (23%) with the narrow and steep lower calyx branch.ConclusionsPrevious studies have reported that the visualization and classification of renal pelvicalyceal anatomical structure by endocast, autopsy, ultrasonography and excretory urography, the modified Takazawa classification system based on 3D virtual reconstruction models enables to standardized different anatomical morphology of renal pelvicalyceal system and provide intuitive and concise information on anatomy, thus leading to the improvement in treatment modality.

Project description:Accurately detecting and examining lung nodules early is key in diagnosing lung cancers and thus one of the best ways to prevent lung cancer deaths. Radiologists spend countless hours detecting small spherical-shaped nodules in computed tomography (CT) images. In addition, even after detecting nodule candidates, a considerable amount of effort and time is required for them to determine whether they are real nodules. The aim of this paper is to introduce a high performance nodule classification method that uses three dimensional deep convolutional neural networks (DCNNs) and an ensemble method to distinguish nodules between non-nodules. In this paper, we use a three dimensional deep convolutional neural network (3D DCNN) with shortcut connections and a 3D DCNN with dense connections for lung nodule classification. The shortcut connections and dense connections successfully alleviate the gradient vanishing problem by allowing the gradient to pass quickly and directly. Connections help deep structured networks to obtain general as well as distinctive features of lung nodules. Moreover, we increased the dimension of DCNNs from two to three to capture 3D features. Compared with shallow 3D CNNs used in previous studies, deep 3D CNNs more effectively capture the features of spherical-shaped nodules. In addition, we use an alternative ensemble method called the checkpoint ensemble method to boost performance. The performance of our nodule classification method is compared with that of the state-of-the-art methods which were used in the LUng Nodule Analysis 2016 Challenge. Our method achieves higher competition performance metric (CPM) scores than the state-of-the-art methods using deep learning. In the experimental setup ESB-ALL, the 3D DCNN with shortcut connections and the 3D DCNN with dense connections using the checkpoint ensemble method achieved the highest CPM score of 0.910. The result demonstrates that our method of using a 3D DCNN with shortcut connections, a 3D DCNN with dense connections, and the checkpoint ensemble method is effective for capturing 3D features of nodules and distinguishing nodules between non-nodules.

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Classification of the colonic splenic flexure based on three-dimensional CT analysis.

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