Preoperative Computed Tomography Imaging Research Articles

Anatomical factor associated with thoracic procedural difficulty in robot-assisted minimally invasive esophagectomy.

Robotic surgical systems with full articulation of instruments, tremor filtering, and motion scaling can potentially overcome the procedural difficulties in endoscopic surgeries. However, whether robot-assisted minimally invasive esophagectomy (RAMIE) can overcome anatomical difficulties during thoracoscopic esophagectomy remains unclear. This study aimed to clarify the anatomical and clinical factors that influence the difficulty of RAMIE in the thoracic region. Forty-five patients who underwent curative-intent RAMIE with upper mediastinal lymph node dissection for esophageal cancer were included. Using preoperative computed tomography images, we calculated previously reported anatomical indices to assess the upper mediastinal narrowness and vertebral body projections in the middle thoracic region. The factors influencing thoracic operative time were then investigated. During the thoracic procedure, the median operative time was 215 (124-367) min and the median blood loss was 20 (5-190) mL. Postoperatively, pneumonia, anastomotic leakage, and recurrent laryngeal nerve palsy occurred in 17.8%, 2.2%, and 6.7% of the patients, respectively. The multiple linear regression model revealed that a narrow upper mediastinum and greater blood loss during the thoracic procedure were significant factors associated with a prolonged thoracic operative time (P = 0.025 and P < 0.001, respectively). Upper mediastinal narrowing was not associated with postoperative complications. A narrow upper mediastinum was significantly associated with a prolonged thoracic operative time in patients with RAMIE.

IMG-32. USER EVALUATION OF AN AI-POWERED WEB APPLICATION TO SUPPORT RADIOGRAPHIC INTERPRETATION OF PATIENTS WITH ADAMANTINOMATOUS CRANIOPHARYNGIOMA

Abstract BACKGROUND In neuro-oncology, accurately interpreting radiographic images, especially for diagnosing central nervous system (CNS) tumors like Adamantinomatous Craniopharyngioma (ACP), presents formidable challenges. Even with advanced imaging technologies such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), pinpointing features related to ACP invasiveness remains daunting. METHODS We introduced an interactive web application that utilizes a custom AI model to analyze preoperative MRI and CT images of 50 patients with ACP. This application can predict a 12-point radiographic feature profile and rank patients based on similarity, aiming to support clinicians in making more precise diagnoses. We deployed our application on a hospital-grade radiographic reading terminal. Our application’s utility and user-friendliness were evaluated through a user study involving four neurosurgeons and two neuroradiologists, who constructed 12-point radiographic feature profiles for ten patients within a 30-minute timeframe given patient MRI and CT images. RESULTS Our study subjects reported high self-assessed numeracy (SNS scores ranging from 4 to 5.25) despite varied preferences in engaging with numerical data. Subjects could efficiently learn to navigate our software interface, with an average task completion time of approximately 3 minutes and 25 seconds per patient. Introducing AI into the task workflow resulted in a slight, statistically non-significant increase in completion time, indicating that while AI modifies interaction dynamics, it does not significantly hinder efficiency in task performance. We observed distinct agreement patterns between clinicians and AI prediction, utilizing Fleiss’ Kappa for categorization. AI assistance subtly boosted clinicians’ confidence and slightly lessened the perceived difficulty of interpreting radiographic images. However, these benefits were not statistically significant. CONCLUSIONS Our study highlights the promise of AI in improving neuro-oncology diagnostics, showing that clinicians quickly adapted to an AI-enhanced application, which subtly influenced their radiographic interpretation. This finding underscores the potential for AI to enhance decision-making accuracy in challenging cases like ACP.

Investigation of the Position and Angle of the Vocal Fold Projected onto the Thyroid Cartilage.

To perform laryngeal framework surgery for unilateral vocal fold paralysis and obtain favorable voice improvement, it is necessary to accurately determine the vocal fold and arytenoid cartilage positions. Thus, the position and angle of the paralyzed vocal folds and arytenoid cartilage projected onto the affected thyroid plate were measured using computed tomography (CT) before and after surgery. Forty-six male patients with thyroid cartilage ossification observed on preoperative CT and vocal fold paralysis were included. Using Adobe Illustrator®, the thyroid plate on the affected side was reconstructed from the continuous images of the sagittal section of the CT examination during participant's quiet breathing (reconstructed affected thyroid plate [RATP]). The anterior commissure mean position was slightly cranial to the midpoint of the thyroid cartilage midline. The paralyzed vocal fold angle was not parallel to the baseline. The average unaffected vocal fold angle during vocalization projected onto the affected thyroid plate was 13.83°, which differed significantly from the average paralyzed vocal fold angle before surgery (19.05°). However, no significant difference was observed in comparison with the average angle of the paralyzed vocal fold after arytenoid adduction. The average distance from the inferior notch of the affected side thyroid cartilage to the affected arytenoid cartilage was 16.7 mm. By understanding the positional relationship between the thyroid cartilage plate and internal structure from preoperative CT images, more effective surgery can be performed according to individual differences. IV Laryngoscope, 2024.

Radiologic tumor border can further stratify prognosis in patients with pancreatic neuroendocrine tumor

Background and objectivesPancreatic neuroendocrine tumor (PanNET), although rare in incidence, is increasing in recent years. Several clinicopathologic and molecular factors have been suggested for patient stratification due to the extensive heterogeneity of PanNETs. We aimed to discover the prognostic role of assessing the tumor border of PanNETs with pre-operative computed tomography (CT) images and correlate them with other clinicopathologic factors. MethodsThe radiologic, macroscopic, and microscopic tumor border of 183 surgically resected PanNET cases was evaluated using preoperative CT images (well defined vs. poorly defined), gross images (expansile vs. infiltrative), and hematoxylin and eosin-stained slides (pushing vs. infiltrative). The clinicopathologic and prognostic significance of the tumor border status was compared with other clinicopathologic factors. ResultsA poorly defined radiologic tumor border was observed in 65 PanNET cases (35.5 %), and were more frequent in male patients (P = 0.031), and tumor with larger size, infiltrative macroscopic growth pattern, infiltrative microscopic tumor border, higher tumor grade, higher pT category, lymph node metastasis, lymphovascular and perineural invasions (all, P < 0.001). Patients with PanNET with a poorly defined radiologic tumor border had significantly worse overall survival (OS) and recurrence-free survival (RFS; both, P < 0.001). Multivariable analysis revealed that PanNET with a poorly defined radiologic border is an independent poor prognostic factor for both OS (P = 0.049) and RFS (P = 0.027). ConclusionPre-operative CT-based tumor border evaluation can provide additional information regarding survival and recurrence in patients with PanNET.

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study.

Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC). In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy. The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not. The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

High Subcutaneous Fat Area Is an Independent Risk Factor for Parastomal Hernia after Transperitoneal Colostomy for Colorectal Cancer.

Parastomal hernia (PSH) is a common complication of colostomy; however, its risk factors remain poorly investigated. In this study, we examined the associations between sarcopenia, visceral and subcutaneous fat, and PSH in patients who underwent transperitoneal colostomy for colorectal cancer. This retrospective, single-center, cohort study included 60 patients who underwent laparoscopic or robot-assisted abdominoperineal resection or Hartmann's procedure for colorectal cancer between November 2010 and February 2022. Stoma creation was uniformly performed using the transperitoneal approach, and PSH was diagnosed via abdominal computed tomography (CT) at 1 year postoperatively. Visceral fat areas (VFAs) and subcutaneous fat areas (SFAs) were measured through preoperative CT images using an image analysis system. Risk factors for PSH were retrospectively analyzed. PSH was diagnosed in 13 (21.7%) patients. In the univariate analysis, PSH was significantly associated with body mass index >22.3 kg/m2 (p=0.002), operation time >319 min (p=0.027), estimated blood loss >230 mL (p=0.008), postoperative complications (p=0.028), stoma diameter >18.6 mm (p=0.015), VFA >89.2 cm2 (p=0.005), and SFA >173.2 cm2 (p=0.001). Multivariate analyses confirmed that SFA >173.2 cm2 (odds ratio: 16.7, 95% confidence interval 1.29-217.2, p=0.031) was an independent risk factor for PSH. Subcutaneous fat area is significantly associated with the development of PSH after transperitoneal colostomy. Applying these insights could help to prevent PSH.

Impact of superior semicircular canal dehiscence on cochlear implant audiologic outcomes

PurposeDetermine whether adult cochlear implant users with radiographic superior semicircular canal dehiscence experience clinically significant differences in audiological outcomes when compared to cochlear implant users with normal temporal bone anatomy. Materials and methodsRetrospective, single institution review. Adult, post-lingual deaf patients implanted between 2010 and 2020. Inclusion criteria included age 18 years or older, available preoperative computed tomography imaging, and preoperative and postoperative AzBio audiological data for at least 6 months of cochlear implant use. Preoperative and postoperative AzBio Sentence Test scores were compared between patients with normal temporal bone anatomy and those with radiographic superior semicircular canal dehiscence or near dehiscence. Results110 patients met inclusion criteria. Mean AzBio score for normal temporal bone anatomy group improved from 35.2 % (SD 28.2) preoperatively to 70.3 % (SD 25.7) postoperatively, an improvement of 35.1 % (SD 28.6). Mean AzBio score for near dehiscent temporal bone anatomy group improved from 26.6 % (SD 28.9) preoperatively to 64.5 % (SD 30.6) postoperatively, an improvement of 37.9 % (SD 27.9). Mean AzBio score for dehiscent temporal bone anatomy group improved from 26.3 % (SD 20.4) preoperatively to 65.1 % (SD 27.6) postoperatively, an improvement of 38.7 % (SD 26.9). Utilizing the one-way analysis of variance test, there was no significant difference in audiologic outcomes between the three groups. ConclusionsPatients with complete or near complete radiographic superior canal dehiscence at the time of cochlear implantation achieve similar improvements in speech perception scores compared to normal anatomy adult cochlear implant users.

Development and validation of a preoperative radiomics-based nomogram to identify patients who can benefit from splenic hilar lymphadenectomy: a pooled analysis of three prospective trials

Background: We aimed to use preoperative computed tomography images to develop a radiomic nomogram to select patients who would benefit from spleen-preserving splenic hilar (No.10) lymphadenectomy (SPSHL). Methods: A pooled analysis of three distinct prospective studies was performed. The splenic hilar lymph node (SHLN) ratio (sLNR) was established as the quotient of the number of metastatic SHLN to the total number of SHLN. Radiomic features reflecting the phenotypes of the primary tumor (RS1) and SHLN region (RS2) were extracted and used as predictive factors for sLNR. Results: This study included 733 patients: 301 in the D2 group and 432 in the D2+No.10 group. The optimal sLNR cutoff value was set at 0.4, and the D2+No.10 group was divided into three groups: sLNR=0, sLNR≤0.4, and sLNR&gt;0.4. Patients in the D2+No. 10 group were randomly divided into the training (n=302) and validation (n=130) cohorts. The AUCs value of the nomogram, including RS1 and RS2, were 0.952 in the training cohort and 0.888 in the validation cohort. The entire cohort was divided into three groups based on the nomogram scores: low, moderate and high SHLN metastasis burden groups (LMB, MMB and HMB, respectively). A similar 5-year OS rate was found between the D2 and D2+No. 10 groups in the LMB and HMB groups. In the MMB group, the 5-year OS of the D2+No. 10 group (73.4%) was significantly higher than that of the D2 group (37.6%) (P&lt;0.001). Conclusions: The nomogram showed good predictive ability for distinguishing patients with various SHLN metastasis burdens. It can accurately identify patients who would benefit from SPSHL.

Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy.

Predicting nonalcoholic fatty liver disease (NAFLD) following pancreaticoduodenectomy (PD) is challenging, which delays therapeutic intervention and makes its prevention difficult. We conducted this study to assess the potential application of preoperative computed tomography (CT) radiomics for predicting NAFLD. The subjects of this retrospective study were 186 patients with PD from a single institution. We extracted the predictors of NAFLD after PD statistically from conventional clinical and radiomic features of the estimated remnant pancreas and whole liver region on preoperative nonenhanced CT images. Based on these predictors, we developed a machine-learning predictive model, which integrated clinical and radiomic features. A comparative model used only clinical features as predictors. The incidence of NAFLD after PD was 43.5%. The variables of the clinicoradiomic model included one shape feature of the pancreas, two texture features of the liver, and sex; the variables of the clinical model were age, sex, and chemoradiotherapy. The accuracy%, precision%, recall%, F1 score, and area under the curve of the two models were 75.0, 72.7, 66.7, 69.6, and 0.80; and 69.6, 68.4, 54.2, 60.5, and 0.69, respectively. Preoperative CT-derived radiomic features from the pancreatic and liver regions are promising for the prediction of NAFLD post-PD. Using these features enhances the predictive model, enabling earlier intervention for high-risk patients.

Prediction of Post-Gastrectomy Pancreatic Complications: A Preoperative Imaging Study Based on Computed Tomography.

Postoperative pancreas-related complications (PPRCs) are common after laparoscopic gastrectomy (LG) in patients with gastric cancer. We estimated the anatomical location of the pancreas on a computed tomography (CT) image and investigated its impact on the incidence of PPRCs after LG. We retrospectively reviewed the preoperative CT images of 203 patients who underwent LG for gastric cancer between January 2010 and December 2017. From these images, we measured the gap between the upper edge of the pancreatic body and the root of the common hepatic artery. We evaluated the potential relationship between PPRCs and the gap between pancreas and common hepatic artery (GPC) status using an analysis based on the median cutoff value and assessed the impact of GPC status on PPRC incidence. We performed univariate and multivariate analyses to identify predictive factors for PPRC. Postoperative pancreas-related complications occurred in 11 patients (5.4%). The median of the optimal cutoff GPC value for predicting PPRC was 0mm; therefore, we classified the GPC status into two groups: GPC plus group and GPC minus group. Univariate analysis revealed that sex (male), C-reactive protein (CRP) > .07mg/dl, GPC plus, and visceral fat area (VFA) > 99cm2 were associated with the development of PPRC. Multivariate analysis identified only GPC plus as independent predictor of PPRC (hazard ratio: 4.60 [95% confidence interval 1.11-31.15], P = .034). The GPC is a simple and reliable predictor of PPRC after LG. Surgeons should evaluate GPC status on preoperative CT images before proceeding with laparoscopic gastric cancer surgery.

Effect of Sarcopenia on the Prognosis of Clinical Outcomes in Patients With Hepatocellular Carcinoma After Hepatic Resection.

We aimed to study the prognostic impact of sarcopenia on overall survival (OS), disease-free survival (DFS), and postoperative outcomes among patients with Hepatocellular carcinoma (HCC) who underwent curative hepatic resection. Data were collected retrospectively from patients with HCC underwent curative hepatic resection and preoperative abdominal computed tomography (CT) at our institution between January 2010 and December 2020. Sarcopenia was evaluated by the skeletal muscle mass at the inferior direction of the third-lumbar-vertebra (L3) cross-sectional area based on preoperative CT imaging using software analysis. Cutoff values for skeletal muscle index (SMI) were 43.75 and 41.10cm2/m2 for males and females. The patients were classified into sarcopenia and nonsarcopenia groups. The association between preoperative sarcopenia and clinicopathological factors, impact of sarcopenia on survival, and postoperative outcomes were analyzed. Sarcopenia was present in 39 of 83 (47.0%) patients who underwent curative hepatic resection for HCC and was significantly correlated with lower SMI, lower serum albumin levels, higher intraoperative blood loss, higher postoperative complications, and longer hospital stay. The 5-year OS was significantly lower in sarcopenic patients than in nonsarcopenic patients (58.2% vs 83.6%; P = .006), but the 5-year DFS was not significantly different between the 2 groups. Multivariate analysis revealed that sarcopenia was a significant risk factor for poor OS (HR 4.728; 95% CI, 1.458-15.329; P = .010). Sarcopenia was identified as a prognostic factor for poor OS after hepatic resection, and major postoperative complications were more frequent in sarcopenia. Early sarcopenia detection and management may improve OS and clinical outcomes in postoperative HCC.

O85: Discrepancy Rates in Acute Abdominal CT: A Comparison of In-House and Remote Reporting to Intra-Operative Laparoscopic and Laparotomy Findings

Abstract Non-traumatic abdominal pain is a common emergency presentation frequently managed by General Surgeons. Abdominopelvic computed tomography (CT) scans are the most popular imaging modality in this context. In many hospitals, the rising demand for urgent and emergent scans out-of-hours has necessitated the outsourcing of this service to teleradiology companies whereby reports are generated at sites remote from the image acquisition. The primary aim of this study was to determine the discrepancy rates of pre-operative CT imaging by source (Teleradiology compared to in-house). This was a retrospective monocentric study conducted at a busy district general hospital over a seven-month period. Patient demographic data, operative notes, and radiology reports (by source) were collated for all patients aged ≥ 16 years presenting with atraumatic abdominal pain who underwent abdominopelvic CT with subsequent surgical intervention (laparoscopy and/or laparotomy). 71 patients were identified by initial screening, 10 patients (11 scans) met the criteria for a ‘major’ discrepancy. Overall discrepancy rates were calculated at 5.6% for scans reported off-site compared to 9.9% for reports generated by in-house radiologists, P = 0.192 (p &amp;lt; 0.05), OR 2.625 (95% CI 0.6915 to 8.595). This study demonstrated lower discrepancy rates in scans reported remotely and can be used as the catalyst for improving aspects of in-house CT reporting.

Association of mid-thigh quadriceps muscle quality with postoperative outcomes in patients with hip osteoarthritis undergoing total hip arthroplasty: a retrospective cohort study.

Muscle quality is more important than muscle mass for assessing physical function. Computed tomography (CT) is used to evaluate intramuscular fatty infiltration. The mid-thigh quadriceps CT attenuation values (CTV) expressed in Hounsfield units (HU) negatively correlate with physical function. Patients with hip osteoarthritis (HOA) have lower extremity muscle atrophy, including decreased cross-sectional area (CSA), CTV, and muscle strength. Using preoperative CT images, we investigated the association between mid-thigh quadriceps CSA, CTV, and postoperative outcomes in patients with HOA. This study included 62 patients who had unilateral HOA (62 hips) and underwent total hip arthroplasty (THA). We investigated the association between preoperative and postoperative Japanese Orthopaedic Association (JOA) hip scores, 12-item Short Form survey (SF-12), mid-thigh quadriceps CSA, and CTV. The mean age was 64.7 ± 10.1years, with 15 men (24.2%), and the mean body mass index was 24.3 ± 4.3kg/m2. Secondary HOA was present in 79.0% of patients. The mean CSA and CTV of the mid-thigh quadriceps on the operative side were 38.8 ± 9.8 cm2 and 40.3 ± 7.8 HU, respectively. Multiple regression analyses adjusted for age and sex showed that preoperative mid-thigh quadriceps CSA was not associated with preoperative and postoperative JOA hip scores or SF-12. The preoperative mid-thigh quadriceps CTV was associated with the postoperative JOA hip score in the gait ability domain and SF-12 in the physical component summary domain. Preoperative muscle quality is associated with postoperative outcomes in patients who have HOA regardless of age and sex.

Sarcopenia impacts perioperative and survival outcomes after esophagectomy for cancer: a multicenter study

BackgroundThe impact of sarcopenia on outcomes after esophagectomy is controversial. Most data are currently derived from Asian populations. This study aimed to correlate sarcopenia to short-term perioperative complication rates and long-term survival and recurrence outcomes. MethodsA retrospective analysis was performed of patients undergoing esophagectomy for cancer from 3 tertiary referral centers in Australia. Sarcopenia was defined using cutoffs for skeletal muscle index (SMI), assessed on preoperative computed tomography images. Outcomes measured included complications, overall survival (OS), and disease-free survival (DFS). ResultsOf 462 patients (78.4% male; median age, 67 years), sarcopenia was evident in 276 (59.7%). Patients with sarcopenia had a higher rate of major (Clavien-Dindo ≥ 3b) complications (27.9% vs 14.5%; P < .001), including higher rates of postoperative cardiac arrythmia (16.3% vs 9.7%; P = .042), pneumonia requiring antibiotics (14.5% vs 9.1%; P = .008), and 30-day mortality (5.1% vs 0%; P = .002). In the sarcopenic group, the median OS was lower (37 months [95% CI, 27.1–46.9] vs 114 months [95% CI, 75.8–152.2]; P < .001), as was the median DFS (27 months [95% CI, 18.9–35.1] vs 77 months [95% CI, 36.4–117.6]; P < .001). Sarcopenia was an independent risk factor for lower survival on multivariate analysis (hazard ratio, 1.688; 95% CI, 1.223–2.329; P = .001). ConclusionPatients with preoperative sarcopenia based on analysis of SMI are at a higher risk of major complications and have inferior survival and oncologic outcomes after esophagectomy for esophageal cancer.

Longitudinal Study on Pre- and Post-Operation CT Imaging for Predicting Pedicle Screw Loosening in Patients with Lumbar Degenerative Disease.

We conducted a longitudinal study to examine the predictive role of risk factors in the occurrence of pedicle screw loosening, assessed through pre- and post-operative computed tomography (CT) scans. A total of 103 patients with degenerative lumbar disease who had undergone L4/5 pedicle screw fixation (involving 412 screws) were included in this study. They were subsequently categorized into two groups-the "loosening group" and the "non-loosening group". The axial and sagittal angles of the screw trajectory in pre- and post-operative CT images were measured, and the deviation angles were computed. Additionally, measurements were taken of the Hounsfield unit (HU) within the screw entry point area, the pedicle, and the vertebral body in preoperative CT images. Logistic regression analysis was employed to ascertain the risk factors influencing the occurrence of screw loosening. Elderly patients who experienced screw loosening tended to have bilateral screw issues at the L5 level (p < 0.005). The HU of the pedicle (p < 0.001), age (p < 0.001), and the axial deviation angle (p = 0.014) were identified as independent factors predicting screw loosening. Additionally, when HU of the pedicle < 126.5 or age ≥ 53.5 years, the axial deviation angle was found to be smaller in the group experiencing screw loosening (p = 0.018 and p = 0.019). Loosening of screws positioned at L5 was found to be more prevalent in elderly patients, particularly exhibiting a bilateral occurrence. Independent predictors of this phenomenon included a low HU value in the pedicle, advanced age in patients, and a substantial axial deviation angle. In the case of elderly patients with a low HU value in the pedicle, a reduced axial surgical deflection was necessitated to prevent the occurrence of screw loosening.

Reproducibility and efficiency of liver volumetry using manual method and liver analysis software

BackgroundFor liver volumetry, manual tracing on computed tomography (CT) images is time-consuming and operator dependent. To overcome these disadvantages, several three-dimensional simulation software programs have been developed; however, their efficacy has not fully been evaluated. MethodsThree physicians performed liver volumetry on preoperative CT images on 30 patients who underwent formal right hepatectomy, using manual tracing volumetry and two simulation software programs, SYNAPSE and syngo.via. The future liver remnant (FLR) was calculated using each method of volumetry. The primary endpoint was reproducibility and secondary outcomes were calculation time and learning curve. ResultsThe mean FLR was significantly lower for manual volumetry than for SYNAPSE or syngo.via; there was no significant difference in mean FLR between the two software-based methods. Reproducibility was lower for the manual method than for the software-based methods. Mean calculation time was shortest for SYNAPSE. For the two physicians unfamiliar with the software, no obvious learning curve was observed for using SYNAPSE, whereas learning curves were observed for using syngo.via. ConclusionsLiver volumetry was more reproducible and faster with three-dimensional simulation software, especially SYNAPSE software, than with the conventional manual tracing method. Software can help even inexperienced physicians learn quickly how to perform liver volumetry.

Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study.

Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis. To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics. This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was calculated for each patient using these important predictors. Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05). A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.

Impact of Deltoid Computer Tomography Image Data on the Accuracy of Machine Learning Predictions of Clinical Outcomes after Anatomic and Reverse Total Shoulder Arthroplasty.

Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2-3 years, and 3-5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.

Preprocedural Computed Tomography Planning for Surgical Aortic Valve Replacement

BackgroundSelection of transcatheter valve size using preprocedural computed tomography (CT) is standardized and well established. However, valve sizing for surgical aortic valve replacement (SAVR) is currently performed intraoperatively by using sizers, which may result in variation among operators and risk for prosthesis-patient mismatch. This study evaluated the usefulness of CT annulus measurement for SAVR valve sizing. MethodsThis study included patients who underwent SAVR using Inspiris or Magna Ease and received preoperative electrocardiogram-gated CT imaging. Starting from June 2022, study investigators applied a CT sizing algorithm using CT-derived annulus size to guide minimum SAVR label size. The final decision of valve selection was left to the operating surgeon during SAVR. The study compared the appropriateness of valve selection (comparing implanted size with CT-predicted size) and prosthesis-patient mismatch rates without aortic root enlargement between 2 cohorts: 102 cases since June 2022 (CT sizing cohort) and 180 cases from 2020 to 2021 (conventional sizing cohort). ResultsImplanted size smaller than CT predicted size and severe prosthesis-patient mismatch were significantly lower by CT sizing than by conventional sizing (12% vs 31% [P = .001] and 0% vs 6% [P = .039], respectively). Interoperator variability was a factor associated with implanted size smaller than CT predicted with conventional sizing, whereas it became nonsignificant with CT sizing. ConclusionsApplying CT sizing to SAVR led to improved valve size selection, less prosthesis-patient mismatch, and less interoperator variability. CT sizing for SAVR could also be used to predict prosthesis-patient mismatch before SAVR and identify patients who need aortic root enlargement.

Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images

IntroductionThe automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels. MethodsTo overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training. Results and discussionBased on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.