Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate\'s position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.

To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.

Lung cancer, also known as lung carcinoma, has a high death rate, but an early diagnosis can substantially reduce this risk. In the current era, prediction models face challenges such as low accuracy, excessive noise, and low contrast. To resolve these problems, an advanced lung carcinoma prediction and risk screening model using transfer learning is proposed. Our proposed model initially preprocesses lung computed tomography images for noise removal, contrast stretching, convex hull lung region extraction, and edge enhancement. The next phase segments the preprocessed images using the modified Bates distribution coati optimization (B-RGS) algorithm to extract key features. The PResNet classifier then categorizes the cancer as normal or abnormal. For abnormal cases, further risk screening determines whether the risk is low or high. Experimental results depict that our proposed model performs at levels similar to other state-of-the-art models, achieving enhanced accuracy, precision, and recall rates of 98.21%, 98.71%, and 97.46%, respectively. These results validate the efficiency and effectiveness of our suggested methodology in early lung carcinoma prediction and risk assessment.

UNASSIGNED: Intra-abdominal infectious complications (IAICs) following minimally invasive gastrectomy (MIG) for cancer sometimes worsen short- and long-term outcomes. In this study, we focused on the minimum umbilicus-vertebra diameter (MUVD) in preoperative computed tomography (CT) images and robotic surgery to prevent severe IAIC occurrence.
UNASSIGNED: A total of 400 patients with gastric cancer who underwent 204 laparoscopic gastrectomy (LG) and 196 robotic gastrectomy (RG) procedures were enrolled in this study. We retrospectively investigated the significance of the MUVD and robotic surgery for preventing severe IAICs following MIG using multivariate and propensity score matching analysis.
UNASSIGNED: The MUVD cutoff value was 84 mm by receiver operating characteristic (ROC) curve using severe IAICs as the end point. The MUVD and visceral fat area (VFA) had significantly higher area under the curve (AUC) than BMI (MUVD vs. BMI, p = 0.032; VFA vs. BMI, p < 0.01). In the multivariate analysis, high MUVD (HR, 9.46; p = 0.026) and laparoscopic surgery (HR, 3.35; p = 0.042) were independent risk factors for severe IAIC occurrence. In the propensity matching analysis between robotic and laparoscopic surgery in the high MUVD group, the RG group tended to have a lower severe IAIC rate than the LG group (0% vs. 9.8%, p = 0.056).
UNASSIGNED: The MUVD was a novel and easy-measuring predictor of severe IAICs following MIG. Robotic surgery should be considered first in patients with gastric cancer having an MUVD value of 84 mm or higher from the perspective of severe IAIC occurrence.

UNASSIGNED: Early-stage lung cancer is typically characterized clinically by the presence of isolated lung nodules. Thousands of cases are examined each year, and one case usually contains numerous lung CT slices. Detecting and classifying early microscopic lung nodules is demanding due to their diminutive dimensions and restricted characterization capabilities. Therefore, a lung nodule classification model that performs well and is sensitive to microscopic lung nodules is needed to accurately classify lung nodules.
UNASSIGNED: This paper uses the Resnet34 network as a basic classification model. A new cascade lung nodule classification method is proposed to classify lung nodules into 6 classes instead of the traditional 2 or 4 classes. It can effectively classify six different nodule types including ground-glass and solid nodules, benign and malignant nodules, and nodules with predominantly ground-glass or solid components.
UNASSIGNED: In this paper, the traditional multi-classification method and the cascade classification method proposed in this paper were tested using real lung nodule data collected in the clinic. The test results demonstrate that the cascade classification method in this study achieves an accuracy of 80.04%, outperforming the conventional multi-classification approach.
UNASSIGNED: Different from the existing methods for categorizing the benign and malignant nature of lung nodules, the approach presented in this paper can classify lung nodules into 6 categories more accurately. At the same time, This paper proposes a rapid, precise, and dependable approach for classifying six distinct categories of lung nodules, which increases the accuracy categorization compared with the traditional multivariate categorization method.

With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.

UNASSIGNED: Despite the existing evidence that waist circumference (WC) provides independent and additive information to BMI when predicting morbidity and mortality, this measurement is not routinely obtained in clinical practice. Using computed tomography (CT) scan images, mobile health (mHealth) has the potential to make this abdominal obesity parameter easily available even in retrospective studies.
UNASSIGNED: This study aimed to develop a mobile app as a tool for facilitating the measurement of WC based on a cross-sectional CT image.
UNASSIGNED: The development process included three stages: determination of the principles of WC measurement from CT images, app prototype design, and validation. We performed a preliminary validity study in which we compared WC measurements obtained both by the conventional method using a tape measurement in a standing position and by the mobile app using the last abdominal CT slice not showing the iliac bone. Pearson correlation, student t tests, and Q-Q and Bland-Altman plots were used for statistical analysis. Moreover, to perform a diagnostic test evaluation, we also analyzed the accuracy of the app in detecting abdominal obesity.
UNASSIGNED: We developed a prototype of the app Measure It, which is capable of estimating WC from a single cross-sectional CT image. We used an estimation based on an ellipse formula adjusted to the gender of the patient. The validity study included 20 patients (10 men and 10 women). There was a good correlation between both measurements (Pearson R=0.906). The student t test showed no significant differences between the two measurements (P=.98). Both the Q-Q dispersion plot and Bland-Altman analysis graphs showed good overlap with some dispersion of extreme values. The diagnostic test evaluation showed an accuracy of 83% when using the mobile app to detect abdominal obesity.
UNASSIGNED: This app is a simple and accessible mHealth tool to routinely measure WC as a valuable obesity indicator in clinical and research practice. A usability and validity evaluation among medical teams will be the next step before its use in clinical trials and multicentric studies.

OBJECTIVE: Estimating the three-dimensional (3D) deformation of the lung is important for accurate dose delivery in radiotherapy and precise surgical guidance in lung surgery navigation. Additional 4D-CT information is often required to eliminate the effect of individual variations and obtain a more accurate estimation of lung deformation. However, this results in increased radiation dose. Therefore, we propose a novel method that estimates lung tissue deformation from depth maps and two CT phases per patient.
METHODS: The method models the 3D motion of each voxel as a linear displacement along a direction vector, with a variable amplitude and phase that depend on the voxel location. The direction vector and amplitude are derived from the registration of the CT images at the end-of-exhale (EOE) and the end-of-inhale (EOI) phases. The voxel phase is estimated by a neural network. Coordinate convolution (CoordConv) is used to fuse multimodal data and embed absolute position information. The network takes the front and side views as well as the previous phase views as inputs to enhance accuracy.
RESULTS: We evaluate the proposed method on two datasets: DIR-Lab and 4D-Lung, and obtain average errors of 2.11 mm and 1.36 mm, respectively. The method achieves real-time performance of less than 7 ms per frame on a NVIDIA GeForce 2080Ti GPU.
CONCLUSIONS: Compared with previous methods, our method achieves comparable or even better accuracy with less CT phases.

UNASSIGNED: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients.
UNASSIGNED: 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People\'s Hospital, Peking University People\'s Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples\' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype.
UNASSIGNED: The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all).
UNASSIGNED: The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients.
UNASSIGNED: This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).

UNASSIGNED: Several cases of inaccurate irradiation in brachytherapy have been reported, occurring similarly to external radiation. Due to a large dose per fraction in brachytherapy, inaccurate irradiation can seriously harm a patient. Although various studies have been conducted, systems that detect inaccurate irradiation in brachytherapy are not as developed as those for external irradiation. This study aimed to construct a system that analyzes the source dwell position during irradiation using computed tomography (CT) scout images. The novelty of the study was that by using CT scout images, high versatility and analysis of absolute coordinates can be achieved.
UNASSIGNED: A treatment plan was designed with an iridium-192 (192Ir) source delivering radiation at two dwell positions in a tandem applicator. CT scout images were taken during irradiation, and acquired under different imaging conditions and applicator geometries. First, we confirmed whether a source was visible in CT scout images. Then, employing in-house MATLAB program, source dwell coordinates were analyzed using the images. An analysis was considered adequate when the resulting source dwell coordinates agreed with the treatment plan within ±1 mm, in accordance with AAPM TG56 guidelines for source dwell position accuracy.
UNASSIGNED: The source dwelling was visible in CT scout image, which was enlarged or reduced depending on applicator geometries. The applicator was enlarged by 127% when 130 mm away from the center of CT gantry. The analysis results using our in-house program were considered adequate; although, analysis parameters required adjustments depending on imaging conditions.
UNASSIGNED: The proposed system can be easily implemented for image-guided brachytherapy and can analyze the absolute coordinates of source dwell position. Therefore, the system could be used for preventing inaccurate irradiation by verifying whether brachytherapy was performed properly.