Diagnostic Computed Tomography Imaging Research Articles

Evaluation of the diagnostic efficacy of computed tomography imaging in trauma patients

Aims: The objective of this study was to assess the efficacy of computed tomography imaging tests ordered for trauma patients in the emergency medicine service and to identify pathologies in the imaging. Methods: A retrospective evaluation was conducted on trauma patients who applied to our hospital's Emergency Medicine Department between 01 January 2023 and 01 October 2023 and underwent diagnostic computed tomography imaging (CT). Results: A total of 4193 CT scans were analysed in 3641 patients. Falls were the most common etiological cause (n=3451, 82.3%). Pathology was detected in 25.87% of CT orders. The most frequently used CT type was cranial CT (n=1687, 40.22%). The highest rate of pathology was found in scapula CT (75%), while the lowest rate was observed in cervical spine CT (13%). The most common pathology was detected in cranial CT (n=457). The highest rate of pathology was found in scapula CT (75%), while the lowest rate was observed in cervical spine CT (13%). Conclusion: The accessibility of relevant specialists to emergency physicians reduces the number of unnecessary CT imaging orders. In the emergency department, physicians should adhere to internationally accepted guidelines when ordering advanced imaging studies.

Implementation of a simulation free palliative Radiation Therapy pathway: An inter-professional collaborative project

The Western New South Wales Local Health District (WNSWLHD) has a significant footprint within the state of New South Wales (NSW). Due to the significant size of the WNSWLHD, patients residing in the local health district face many barriers to receiving Radiation Therapy. The inter-professional collaboration behind the successful implementation and evaluation of a simulation free pathway for palliative Radiation Therapy in WNSWLHD will be explored within this narrative. The process known in WNSWLHD as Medical Imaging Simulated Radiation Therapy (MISRT) removes the need for a computed tomography (CT) simulation and allows for palliative Radiation Therapy to be planned on previously acquired diagnostic CT imaging. The implementation of MISRT has required significant inter-professional collaboration. This also extends to inter-centre partnership, intra-district cooperation with Medical Imaging and inter-discipline teamwork amongst Radiation Oncologists, Radiation Oncology Medical Physicists, Radiation Oncology Nursing and Radiation Therapists. The impact and involvement of the inter-disciplinary teamwork will be explored. The methods of implementing MISRT will be outlined covering changes to standard workflow, educational requirements and inclusion criteria. The evaluation of the implementation will also be covered with two evaluation surveys being conducted. Inter-professional communication, education, teamwork and collaboration is highlighted to demonstrate improving access to quality care in a rural and regional healthcare setting whilst reducing known barriers to accessing Radiation Oncology.

Abstract 3405: CT-assessed body composition and tumor immunologic characteristics in patients with non-small-cell lung cancer

Abstract Objectives: Obesity is associated with higher immunotherapy efficacy, but body mass index is a convoluted marker of adipose and muscle tissue. We conducted a discovery scale study to assess the association of skeletal muscle and adipose tissue areas annotated on computed tomography (CT) scan images with selected tumor immunologic characteristics among patients with non-small-cell lung cancer (NSCLC). Methods: We analyzed publicly available data from 102 patients (mean age = 67.2, 47.1% female, and 70.6% stage I-II) who had diagnostic CT images in The Cancer Image Archive. All patients had the 12th thoracic vertebra (T12) image, and a subset (62 patients) had the 3rd lumber vertebra (L3) image. Paravertebral muscle areas on T12 and skeletal muscle and adipose tissue areas on L3 were annotated using Slice-O-Matic. RNA-seq data were provided by The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium; percentages of CD8, regulatory T cells, and activated NK cells were identified using the CIBERSORT. ESTIMATE algorithm was used to derive the stromal score and the immune score. Differences in the immune markers were examined using ANOVA adjusting for stage, histology, and study. Results and Conclusion: Patients with higher paravertebral muscle area (3rd tertile) had the lowest stromal scores, compared to the 2nd and 1st tertile (P<0.05). Patients in the 2nd tertile of paravertebral muscle areas had the highest immune score and CD274 (PD-L1) gene expression. Among the body composition types classified using L3 images, low muscle/high adiposity was associated with lower stromal scores, and low muscle was associated with lower immune scores, compared to high adiposity and high muscle/low adiposity types. Other immunological markers did not differ across the body composition types. Significance: These findings suggest associations between deconvoluted skeletal and adipose tissue components and tumor markers relevant to immune checkpoint inhibitor efficacies in NSCLC. Further validation using immune phenotyping in a large patient sample is warranted. Funding: The study is in part supported by NIH/NCI R37CA248371 and OSU Comprehensive Cancer Center - James. Citation Format: Ting-Yuan David Cheng, Cuthbert Mario Mahenge, Rand T. Akasheh, Ayse Selen Yilmaz, Ben Kinder, Xuan Nguyen, Maciej Pietrzak, Carolyn J. Presley, Peter G. Shields. CT-assessed body composition and tumor immunologic characteristics in patients with non-small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3405.

Diagnostic Utility of Glial Fibrillary Acidic Protein Beyond 12 Hours After Traumatic Brain Injury: A TRACK-TBI Study.

Blood levels of glial fibrillary acidic protein (GFAP) and ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1) within 12h of suspected traumatic brain injury (TBI) have been approved by the Food and Drug administration to aid in determining the need for a brain computed tomography (CT) scan. The current study aimed to determine whether this context of use can be expanded beyond 12h post-TBI in patients presenting with Glasgow Coma Scale (GCS) 13-15. The prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled TBI participants aged ≥17 years who presented to a United States Level 1 trauma center and received a clinically indicated brain CT scan within 24h post-injury, a blood draw within 24h and at 14 days for biomarker analysis. Data from participants with emergency department arrival GCS 13-15 and biomarker values at days 1 and 14 were extracted for the primary analysis. A subgroup of hospitalized participants with serial biomarkers at days 1, 3, 5, and 14 were analyzed, including plasma GFAP and UCH-L1, and serum neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B). The primary analysis compared biomarker values dichotomized by head CT results (CT+/CT-). Area under receiver-operating characteristic curve (AUC) was used to determine diagnostic accuracy. The overall cohort included 1142 participants with initial GCS 13-15, with mean age 39.8 years, 65% male, and 73% Caucasian. The GFAP provided good discrimination in the overall cohort at days 1 (AUC = 0.82) and 14 (AUC = 0.72), and in the hospitalized subgroup at days 1 (AUC = 0.84), 3 (AUC = 0.88), 5 (AUC = 0.82), and 14 (AUC = 0.74). The UCH-L1, NSE, and S100B did not perform well (AUC = 0.51-0.57 across time points). This study demonstrates the utility of GFAP to aid in decision-making for diagnostic brain CT imaging beyond the 12h time frame in patients with TBI who have a GCS 13-15.

Reduction of contrast medium for transcatheter aortic valve replacement planning using a spectral detector CT: a prospective clinical trial

IntroductionThis study investigated the use of dual-energy spectral detector computed tomography (CT) and virtual monoenergetic imaging (VMI) reconstructions in pre-interventional transcatheter aortic valve replacement (TAVR) planning. We aimed to determine the minimum required contrast medium (CM) amount to maintain diagnostic CT imaging quality for TAVR planning.MethodsIn this prospective clinical trial, TAVR candidates received a standardized dual-layer spectral detector CT protocol. The CM amount (Iohexol 350 mg iodine/mL, standardized flow rate 3 mL/s) was reduced systematically after 15 patients by 10 mL, starting at 60 mL (institutional standard). We evaluated standard, and 40- and 60-keV VMI reconstructions. For image quality, we measured signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and diameters in multiple vessel sections (i.e., aortic annulus: diameter, perimeter, area; aorta/arteries: minimal diameter). Mixed regression models (MRM), including interaction terms and clinical characteristics, were used for comparison.ResultsSixty consecutive patients (mean age, 79.4 ± 7.5 years; 28 females, 46.7%) were included. In pre-TAVR CT, the CM reduction to 40 mL is possible without affecting the image quality (MRM: SNR: –1.1, p = 0.726; CNR: 0.0, p = 0.999). VMI 40-keV reconstructions showed better results than standard reconstructions with significantly higher SNR (+ 6.04, p < 0.001). Reduction to 30 mL CM resulted in a significant loss of quality (MRM: SNR: –12.9, p < 0.001; CNR: –13.9, p < 0.001), regardless of the reconstruction. Across the reconstructions, we observed no differences in the metric evaluation (p > 0.914).ConclusionAmong TAVR candidates undergoing pre-interventional CT at a dual-layer spectral detector system, applying 40 mL CM is sufficient to maintain diagnostic image quality. VMI 40-keV reconstructions improve the vessel attenuation and are recommended for evaluation.Clinical relevance statementContrast medium reduction to 40 mL in pre-interventional transcatheter aortic valve replacement CT using dual-energy CT maintains image quality, while 40-keV virtual monoenergetic imaging reconstructions enhance vessel attenuation. These results offer valuable recommendations for interventional transcatheter aortic valve replacement evaluation and potentially improve nephroprotection in patients with compromised renal function.Key Points• Patients undergoing transcatheter aortic valve replacement (TAVR), requiring pre-interventional CT, are often multimorbid with impaired renal function.• Using a spectral detector dual-layer CT, contrast medium reduction to 40 mL is feasible, maintaining diagnostic image quality.• The additional application of virtual monoenergetic image reconstructions with 40 keV improves vessel attenuation significantly in clinical practice.

Relationship between circulating tumour DNA and skeletal muscle stores at diagnosis of pancreatic ductal adenocarcinoma: a cross-sectional study

Low skeletal muscle index (SMI) and low skeletal muscle radiodensity (SMD) are associated with reduced survival time in pancreatic ductal adenocarcinoma (PDAC). The negative prognostic impact of low SMI and low SMD is often reported as independent of cancer stage when using traditional clinical staging tools. Therefore, this study sought to explore the relationship between a novel marker of tumour burden (circulating tumour DNA) and skeletal muscle abnormalities at diagnosis of PDAC. A retrospective cross-sectional study was conducted in patients who had plasma and tumour tissue samples stored in the Victorian Pancreatic Cancer Biobank (VPCB) at diagnosis of PDAC, between 2015 and 2020. Circulating tumour DNA (ctDNA) of patients with G12 and G13 KRAS mutations was detected and quantified. Pre-treatment SMI and SMD derived from analysis of diagnostic computed tomography imaging was tested for its association to presence and concentration of ctDNA, as well as conventional staging, and demographic variables. The study included 66 patients at PDAC diagnosis; 53% female, mean age 68.7 years (SD ± 10.9). Low SMI and low SMD were present in 69.7% and 62.1% of patients, respectively. Female gender was an independent risk factor for low SMI (OR 4.38, 95% CI 1.23–15.55, p = 0.022), and older age an independent risk factor for low SMD (OR 1.066, 95% CI 1.002–1.135, p = 0.044). No association between skeletal muscle stores and concentration of ctDNA (SMI r = − 0.163, p = 0.192; SMD r = 0.097, p = 0.438) or stage of disease according to conventional clinical staging [SMI F(3, 62) = 0.886, p = 0.453; SMD F(3, 62) = 0.717, p = 0.545] was observed. These results demonstrate that low SMI and low SMD are highly prevalent at diagnosis of PDAC, and suggest they are comorbidities of cancer rather than related to the clinical stage of disease. Future studies are needed to identify the mechanisms and risk factors for low SMI and low SMD at diagnosis of PDAC to aid screening and intervention development.

Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.

Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography (CT) scans, we developed a deep learning (DL)-based computer-aided detection (CAD) system. We retrospectively analyzed 2125 diagnostic and radiotherapeutic CT images of 79 patients. Images annotated as tumor (positive) or not (negative) were randomized into training (1782 images) and test (343 images) datasets. YOLOv5m architecture was used to detect vertebra on whole CT scans. InceptionV3 architecture with the transfer-learning technique was used to classify the presence/absence of lytic lesions on CT images showing the presence of vertebra. The DL models were evaluated via fivefold cross-validation. For vertebra detection, bounding box accuracy was estimated using intersection over union (IoU). We evaluated the area under the curve (AUC) of a receiver operating characteristic curve to classify lesions. Moreover, we determined the accuracy, precision, recall, and F1 score. We used the gradient-weighted class activation mapping (Grad-CAM) technique for visual interpretation. The computation time was 0.44s per image. The average IoU value of the predicted vertebra was 0.923 ± 0.052 (0.684-1.000) for test datasets. In the binary classification task, the accuracy, precision, recall, F1-score, and AUC value for test datasets were 0.872, 0.948, 0.741, 0.832, and 0.941, respectively. Heat maps constructed using the Grad-CAM technique were consistent with the location of lytic lesions. Our artificial intelligence-aided CAD system using two DL models could rapidly identify vertebra bone from whole CT images and detect lytic spinal bone metastasis, although further evaluation of diagnostic accuracy is required with a larger sample size.

Final Analysis of Diagnostic Endoscopic Resection Followed by Selective Chemoradiotherapy for Stage I Esophageal Cancer: JCOG0508

Final Analysis of Diagnostic Endoscopic Resection Followed by Selective Chemoradiotherapy for Stage I Esophageal Cancer: JCOG0508

Anatomy-based prediction method for determining ipsilateral lung doses in postoperative breast radiation therapy assisted by diagnostic computed tomography images.

BackgroundThis study aimed to investigate whether ipsilateral lung doses (ILDs) could be predicted by anatomical indexes measured using diagnostic computed tomography (CT) prior to the planning stage of breast radiation therapy (RT).Materials and methodsThe thoracic diameters and the length of lines drawn manually were measured on diagnostic CT images. The parameters of interest were the skin maximum lung distance (sMLD), central lung distance (CLD), Haller index (HI), and body mass index (BMI). Lung dose-volume histograms were created with conformal planning, and the lung volumes receiving 5–40 Gy (V5–V40) were calculated. Linear regression models were used to investigate the correlations between the anatomical indexes and dose differences and to estimate the slope and 95% confidence intervals (CIs).ResultsA total of 160 patients who had undergone three-dimensional conformal RT after breast-conserving surgery were included. Univariable analysis revealed that the sMLD (p < 0.001), CLD (p < 0.001), HI (p = 0.002), and BMI (p < 0.001) were significantly correlated with the V20. However, multivariable analysis revealed that only the sMLD (slope: 0.147, p = 0.001, 95% CI: 0.162–0.306) and CLD (0.157, p = 0.005, 0.048–0.266) were strongly correlated with the V20. The p-value for the sMLD was the lowest among the p-values for all indexes, thereby indicating that the sMLD had the best predictive power for ILD.ConclusionssMLD and CLD are anatomical markers that can be used to predict ILD in whole breast RT. An sMLD > 20.5 mm or a CLD > 24.3 mm positively correlated with a high ILD.

An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check

Complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with incorrect level treatments of the spine. The purpose of this work was to mitigate such challenges by fully automating the treatment planning process for diagnostic and simulation computed tomography (CT) scans. Vertebral bodies are labeled on CT scans of any length using 2 intendent deep-learning models-mirroring 2 different experts labeling the spine. Then, a U-Net++ architecture was trained, validated, and tested to contour each vertebra (n=220 CT scans). Features from the CT and auto-contours were input into a random forest classifier to predict whether vertebrae were correctly labeled. This classifier was trained using auto-contours from cone beam computed tomography, positron emission tomography/CT, simulation CT, and diagnostic CT images (n=56 CT scans, 751 contours). Auto-plans were generated via scripting. Each model was combined into a framework to make a fully automated clinical tool. A retrospective planning study was conducted in which 3 radiation oncologists scored auto-plan quality on an unseen patient cohort (n=60) on a 5-point scale. CT scans varied in scan length,presence of surgicalimplants, imagingprotocol,andmetastatic burden. The results showed that the uniquely designed convolutional neural networks accurately labeled and segmented vertebral bodies C1-L5 regardless of imaging protocol or metastatic burden. Mean dice-similarity coefficientwas85.0% (cervical),90.3% (thoracic), and93.7% (lumbar).The randomforestclassifier predicted mislabelingacrossvariousCT scan types with an area under the curve of 0.82.All contouring and labeling errorswithintreatmentregions (11 of 11), includingerrors frompatient plans with atypical anatomy(eg,T13, L6) were detected.Radiation oncologists scored98%of simulation CT-based plansand 92% ofdiagnostic CT-basedplans as clinically acceptableor needingminor edits for patients with typical anatomy.On average, end-to-end treatment planning time of the clinical tool was less than 8 minutes. This novel method to automatically verify, contour, and plan palliative spine treatments is efficient and effective acrossvariousCT scan types.Furthermore, it is the first to create a clinical tool that can automatically verify vertebral level in CT images.

Dosimetric effects of embolization material artefacts in arteriovenous malformations stereotactic radiosurgery on treatment planning calculation.

Background and PurposeStereotactic Radiosurgery (SRS) is a specialized radiotherapy treatment technique for Arteriovenous Malformations (AVM) in which Computed Tomography (CT) images are used for dose calculations. The purpose of this study was to investigate CT image distortions caused by embolic agents and quantify the influence of these distortions on dose calculations.MethodsEight AVM patients administered embolic agents prior to SRS were included. Original plans were compared to new recalculated plans using two sets of images. The first set was created by masking the embolic material and artefacts, the second was the diagnostic CT images. In addition, treatment plans were created for an anthropomorphic phantom with water inserts, then with known volumes of embolic materials to study the dosimetric effect of each material.ResultsRelative to patients’ original plans, maximum Monitor Unit (MU) difference was −4.4% with whole brain masking, −1.3% with artefact masking, −4.1% with embolic masking, and −4.5% with artefact-free diagnostic images. Calculated dose differences were within ± 3.5% for all plans. In phantom, Gamma pass rate was 96% for both embolic agents with conformal fields and 99.9% with dynamic arcs. Dose and MU differences in phantom plans were negligible.ConclusionRelative dose differences between the original plans and the corrected ones were not clinically remarkable. We recommend evaluating the effect of embolic materials on individual patients’ plans. The whole brain corrected planning CT images or diagnostic CT images could be utilized to calculate the magnitude of dose reduction caused by embolic materials and correct it if necessary.

Clinical Features and Body Composition in Men with Hormone-Sensitive Metastatic Prostate Cancer: A Pilot Study Examining Differences by Race.

Black men treated with frontline therapies for metastatic prostate cancer (MPC) show better clinical outcomes than non-Black men receiving similar treatments. Variations in body composition may contribute to these findings. However, preliminary data are required to support this concept. We conducted a retrospective cohort study for all men with MPC evaluated at our center over a 4-year period, collecting demographic and clinical data (N = 74). Of these, 55 men had diagnostic computed tomography images to quantify adipose tissue and skeletal muscle, specifically sarcopenia and myosteatosis. Nineteen men had repeat imaging to explore changes over time. Frequencies, medians, interquartile ranges, and time to event analyses (hazard ratios (HR); confidence interval (CI)) are presented, stratified by race. Overall, 49% (n = 27) of men had sarcopenia, 49% (n = 27) had myosteatosis, and 29% (n = 16) had sarcopenia and myosteatosis simultaneously. No significant relationship between body mass index (Log-rank p=0.86; HR: 1.05, 95% CI: 0.45–2.49) or sarcopenia (Log-rankp=0.92; HR: 1.01, 95% CI: 0.46–2.19) and overall survival was observed. However, the presence of myosteatosis at diagnosis was associated with decreased overall survival (Log-rank p=0.09; HR: 2.34, 95% CI: 1.05–5.23), with more pronounced (statistically nonsignificant) negative associations for Black (HR: 4.39, 95% CI: 0.92–21.1, p=0.06) versus non-Black men (HR: 1.89, 95% CI: 0.79–4.54, p=0.16). Over the median 12.5 months between imaging, the median decline in skeletal muscle was 4% for all men. Black men displayed a greater propensity to gain more adipose tissue than non-Black men, specifically subcutaneous (p=0.01). Because of the potential for Type II errors in this pilot, future studies should seek to further evaluate the implications of body composition on outcomes. This will require larger, adequately powered investigations with diverse patient representation.

Validation of automated body composition analysis using diagnostic computed tomography imaging in patients with pancreatic cancer

BackgroundSarcopenia is associated with complications and inferior oncologic outcomes in solid tumors. Axial computed tomography (CT) scans can be used to evaluate sarcopenia, however manual quantification is laborious. We sought to validate an automated method of quantifying muscle cross-sectional area (CSA) in patients with pancreatic adenocarcinoma (PDAC). MethodsMid-L3 CT images from patients with PDAC were analyzed: CSAs of skeletal muscle (SM) were measured using manual segmentation and the software AutoMATiCA, and then compared with linear regression. ResultsFive-hundred-twenty-five unique scans were analyzed. There was robust correlation between manual and automated segmentation for L3 CSA (R2 0.94, P < 0.001). Bland-Altman analysis demonstrated a consistent overestimation of muscle CSA by AutoMATiCA with a mean difference of 5.7%. A correction factor of 1.06 was validated using a unique test dataset of 36 patients with non-PDAC peripancreatic malignancies. ConclusionsAutomated muscle CSA measurement with AutoMATiCA is highly efficient and yields results highly correlated with manual measurement. These findings support the potential use of high-throughput sarcopenia analysis with abdominal CT scans for both clinical and research purposes.

A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer.

The purpose of this study was to explore the deep learning radiomics (DLR) nomogram to predict the overall 3-year survival after chemoradiotherapy in patients with esophageal cancer. The 154 patients' data were used in this study, which was randomly split into training (116) and validation (38) data. Deep learning and handcrafted features were obtained via the preprocessing diagnostic computed tomography images. The selected features were used to construct radiomics signatures through the least absolute shrinkage and selection operator (LASSO) regression, maximizing relevance while minimizing redundancy. The DLR signature, handcrafted features' radiomics (HCR) signature, and clinical factors were incorporated to develop a DLR nomogram. The DLR nomogram was evaluated in terms of discrimination and calibration with comparison to the HCR signature-based radiomics model. The experimental results showed the outperforming discrimination ability of the proposed DLR over the HCR model in terms of Harrel's concordance index, 0.76 and 0.784, for training and validation sets, respectively. Also, the proposed DLR nomogram calibrates and classifies better than the HCR model in terms of AUC, 0.984 (vs. 0.797) and 0.942 (vs. 0.665) for training and validation sets, respectively. Furthermore, the nomogram-predicted Kaplan–Meier survival (KMS) curves differed significantly from the nonsurvival groups in the log-rank test (p value <0.05). The proposed DLR model based on conventional CT images showed the outperforming performance over the HCR signature model in noninvasively individualized prediction of the 3-year survival rate in esophageal cancer patients. The proposed model can potentially provide prognostic information that guides and helps the clinical decisions between observation and treatment.

Evaluation of Ultra-low-dose (ULD) Lung Computed Tomography (CT) Using Deep-learning: A Phantom Study

As an electromagnetic wave, X-rays are used to acquire diagnostic CT images. The aim of this phantom study was to evaluate the image quality of ultra-low-dose (ULD) lung computed tomography (CT) achieved using a deep-learning based image reconstruction method. The chest phantom was scanned with a tube voltage of 100 kV for various CT dose index (CTDIvol) conditions: 0.4 mGy for ultra-low-dose (ULD), 0.6 mGy for low-dose (LD), 2.7 mGy for standard (SD), and 7.1 mGy for large size (LS). The signal-to-noise ratio (SNR) and noise values in reconstructions produced via filtered back projection (FBP), iterative reconstruction (IR), and deep convolutional neural network (DCNN) were computed for comparison. The quantitative results of both the SNR and noise indicate that the adoption of the DCNN makes the image reconstruction in the ULD setting more stable and robust, achieving a higher image quality when compared with the FBP algorithm in the SD condition. Compared with the conventional FBP and IR, the proposed deep learning-based image reconstruction approach can improve the ULD CT image quality while significantly reducing the patient dose.

Skeletal muscle radiodensity and visceral adipose tissue index are associated with survival in renal cell cancer – A multicenter population-based cohort study

Body composition has been associated with disease outcome in several cancer types. Results for localized and metastatic renal cell cancer (RCC) are limited and inconsistent. Our aim was to examine the association between body composition and survival in RCC. We conducted a population-based historical cohort study including patients diagnosed with RCC from 2008 to 2012. Diagnostic Computed Tomography images at the third lumbar vertebra (L3) were assessed for skeletal muscle index (SMI), skeletal muscle density (SMD), visceral adipose tissue index (VATI) and subcutaneous adipose tissue index (SATI). Clinical data was retrieved from medical records. Multivariable Cox regressions with restricted cubic splines were used to determine hazard ratios (HRs) and 95% confidence intervals (95%CIs) for 10-unit increases in body composition features with overall survival (OS) and recurrence-free survival (RFS). We included 719 stage I-III (of whom 254 (35.3%) died and 148 (21.9%) experienced recurrence) and 320 stage IV RCC patients (of whom 298 (93.1%) died). Median follow-up was 6.35 years (interquartile range; 1.41-8.23). For stage I-III, higher SMD was associated with better OS (men: HR 0.86; 95% CI 0.68-1.08; women: HR 0.69; 95% CI 0.50-0.95). Lower compared to median VATI was associated with worse OS for both men (HR 1.38; 95%CI 1.05-1.83 for VATI=25) and women (HR 1.67; 95%CI 1.01-2.78 for VATI=20). For stage IV, higher SMD and higher VATI were associated with better OS among men (HR 0.74; 95% CI 0.59-0.94 and HR 0.93; 95% CI 0.88-0.99, respectively). Results for women were similar but non-significant. No statistically significant associations were found for SMI or SATI. Higher SMD and higher VATI were marginally associated with better survival in RCC patients and might be useful for better prognostication. However, the added value to current prognostic scores needs to be investigated.

Impact of subcutaneous and visceral fat adiposity in patients with colorectal cancer

Although different body composition including fat adiposity has known to be associated with survival in patients with colorectal cancer (CRC), the clinical significance was inconsistent. We investigated prognostic impact of visceral and subcutaneous fat adiposity in patients with CRC after surgical resection. This retrospective single center study included 987 stage I-III CRC patients (583 males, and 404 females) who underwent surgical resection between March 2005 and April 2014. Preoperative diagnostic computed tomography images were used to quantify visceral fat area (VFA) and subcutaneous fat area (SFA). The sex-specific optimal cut-off value for body fat composition was defined using the X-tile program. The Cox proportional hazards model was used to determine the correlation fat composition and disease-free survival (DFS). Harrell's concordance index (C-index) and integrated area under curve (iAUC) were used to evaluate the predictive ability of cut-derived stratification. In univariate analysis, high SFA (≥141.73cm2 in males and ≥168.71cm2 in females) and high VFA (≥174.38cm2 in male and ≥83.65cm2 in female) were identified as significant prognostic factors for better DFS (p=.001 and p=.003 respectively). However, multivariate analysis revealed that high SFA independently predicted longer DFS (HR 0.505; 95% CI 0.266-0.957; p=.036) whereas, high VFA did not (HR 0.656; 95% CI 0.402-1.071; p=.092). Combining stage and SFA-cutoff showed better discriminatory performance than the model using stage solitary with respect to C-index (0.667; 95% CI 0.623-6.711; p=.0098) and iAUC (0.601; 95% CI 0.556-0.620). High SFA was correlated with better DFS in patients with CRC. Subcutaneous fat can have additive predictive capability when incorporated into clinical decision-making.

Dosimetry Comparison of Palliative Radiation Plans Generated From Available Diagnostic CT Images Versus Dedicated CT Simulation for Inpatients.

IntroductionThe morbidity sequelae of advanced cancer are often irreversible. Early palliative radiation can prevent, delay, and even improve these consequences. Treatment may be delayed due to a packed computed tomography (CT) simulation schedule or other logistics, including the cost and burden of arranging ambulance transportation when radiation centers are off-site.ObjectivesThe primary objective was to determine the feasibility of using a recent diagnostic CT scan in lieu of a dedicated simulation CT to generate an adequate plan without sacrificing dosimetric goals and subsequent efficacy or tolerability. Secondary objectives included how much the lesion has grown, and how much earlier treatment could start if planned on a diagnostic CT scan.Materials/MethodsFor each inpatient treated with palliative radiation, a prior recent diagnostic CT scan was imported into the RayStation (RaySearch Laboratories, Stockholm, Sweden) planning system. From these diagnostic scans, planning treatment volumes (PTV) and organs at risk (OAR) were contoured using the same technique as the patient’s actual treatment. The primary outcome was to compare both the PTV coverage and OAR dose between the plan generated from the diagnostic CT compared to that from the simulation CT. Our secondary outcomes include the mean time between CT simulation and first treatment, change in tumor volume between diagnostic scan and CT simulation, and the hottest 1% of each plan (D1).ResultsBetween May and August 2019, a total of 22 inpatients were treated palliatively. Of those 22 patients, 10 patients (ages 32-92 years, median 64.5 years, 50% spine) met study criteria and had a diagnostic CT scan that was obtained within 14 days of simulation CT that was also compatible with our planning software. In the plans that were delivered, a mean of 98.8% (range 94.4-100%) of PTV was covered by at least 95% prescription dose. In the diagnostic CT plans, a mean of 95.4% (range 84.5-100%) of PTV was covered by at least 95% prescription dose. The difference between plans trended towards significance (p=0.061). When looking at patients receiving treatment to the spine or having a diagnostic CT within four days of the simulation CT, there was no statistically significant difference between the two plans (p=0.032 and 0.030, respectively). The OARs received, on average, 1.4% less mean radiation dose in the hypothetical plans (p=0.911). All OAR constraints were met in both groups. The mean time between diagnostic CT and CT simulation was 5.9 days and between CT simulation and first treatment was 1.9 days (range 0-5 days). The mean change in tumor volume was 22.64% smaller in the diagnostic CT scan plan. The D1 was an average 1% hotter in the hypothetical plans (p=0.16).ConclusionIn hospitalized patients with an indication for palliative radiation, treatment planning on a pre-existing recent diagnostic CT scan produces comparable dose distributions without increases in dose to OARs when compared to the use of CT simulation scans, particularly for the treatment of the spine or when a very recent diagnostic CT is available. Bypassing CT simulation in select cases allows for earlier delivery of radiation with less patient and logistical burden. In combination with daily image guidance, this may translate to more timely delivery of radiation, less cost and burden to critically ill patients, and improved palliative benefit.

Establishing a survival prediction model for esophageal squamous cell carcinoma based on CT and histopathological images

Currently, the incidence of esophageal squamous cell carcinoma (ESCC) in China is high and its prognosis is poor. To evaluate the prognosis of patients with ESCC, we performed computerized quantitative analyses on diagnostic computed tomography (CT) and digital histopathological slices. A retrospective study was conducted to assess the prognosis of ESCC in 153 patients who underwent esophagectomy, and the cohort was selected based on strict clinical criteria. Each patient had an enhanced CT image, and there were two imaging protocols for CT images of all patients. Each patient in the cohort also had a histopathological tissue slide after hematoxylin–eosin staining. Under an electron microscope, the tissue slide was scanned as an image of large size. We then performed quantitative analyses to identify factors related to the prognosis of ESCC on digital histological images and diagnostic CT images. For CT images, we used the radiomics method. For histological images, we designed a set of quantitative features based on machine learning algorithms, such as K-means and principal component analysis. These features describe the patterns of different cell types in histopathological images. Subsequently, we used the survival analysis model established using only CT image features as the baseline. We also compared multiple machine learning models and adopted a five-fold cross-validation method to establish a robust survival model. In establishing survival models, we first used CT image features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.624. Then we used histopathlogical features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.664, which was obviously better than CT’s. Lastly, we combined CT image features and histopathological image features to establish survival models. The performance was better than that in the models built using only CT image features or histopathological image features, and the C-index from the regularized Cox model on the test set reached 0.694. We also proved the effectiveness of the quantified histopathological image features in terms of prognosis using the log-rank test. Histopathological image features are more relevant to prognosis than features extracted from CT images using radiomics. The results of this study provide clinicians with a reference to improve the survival rate of patients with ESCC after surgery. These results have implications for advancing the process of explaining the poor prognosis of esophageal cancer.

Analysis of Delayed Surgical Treatment and Oncologic Outcomes in Clinical Stage I Non–Small Cell Lung Cancer

The association between delayed surgical treatment and oncologic outcomes in patients with non-small cell lung cancer (NSCLC) is poorly understood given that prior studies have used imprecise definitions for the date of cancer diagnosis. To use a uniform method to quantify surgical treatment delay and to examine its association with several oncologic outcomes. This retrospective cohort study was conducted using a novel data set from the Veterans Health Administration (VHA) system. Included patients had clinical stage I NSCLC and were undergoing resection from 2006 to 2016 within the VHA system. Time to surgical treatment (TTS) was defined as the time between preoperative diagnostic computed tomography imaging and surgical treatment. We evaluated the association between TTS and several delay-associated outcomes using restricted cubic spline functions. Data analyses were performed in November 2021. Wait time between cancer diagnosis and surgical treatment (ie, TTS). Several delay-associated oncologic outcomes, including pathologic upstaging, resection with positive margins, and recurrence, were assessed. We also assessed overall survival. Among 9904 patients who underwent surgical treatment for clinical stage I NSCLC, 9539 (96.3%) were men, 4972 individuals (50.5%) were currently smoking, and the mean (SD) age was 67.7 (7.9) years. The mean (SD) TTS was 70.1 (38.6) days. TTS was not associated with increased risk of pathologic upstaging or positive margins. Recurrence was detected in 4158 patients (42.0%) with median (interquartile range) follow-up of 6.15 (2.51-11.51) years. Factors associated with increased risk of recurrence included younger age (hazard ratio [HR] for every 1-year increase in age, 0.992; 95% CI, 0.987-0.997; P = .003), higher Charlson Comorbidity Index score (HR for every 1-unit increase in composite score, 1.055; 95% CI, 1.037-1.073; P < .001), segmentectomy (HR vs lobectomy, 1.352; 95% CI, 1.179-1.551; P < .001) or wedge resection (HR vs lobectomy, 1.282; 95% CI, 1.179-1.394; P < .001), larger tumor size (eg, 31-40 mm vs <10 mm; HR, 1.209; 95% CI, 1.051-1.390; P = .008), higher tumor grade (eg, II vs I; HR, 1.210; 95% CI, 1.085-1.349; P < .001), lower number of lymph nodes examined (eg, ≥10 vs <10; HR, 0.866; 95% CI, 0.803-0.933; P < .001), higher pathologic stage (III vs I; HR, 1.571; 95% CI, 1.351-1.837; P < .001), and longer TTS, with increasing risk after 12 weeks. For each week of surgical delay beyond 12 weeks, the hazard for recurrence increased by 0.4% (HR, 1.004; 95% CI, 1.001-1.006; P = .002). Factors associated with delayed surgical treatment included African American race (odds ratio [OR] vs White race, 1.267; 95% CI, 1.112-1.444; P < .001), higher area deprivation index [ADI] score (OR for every 1 unit increase in ADI score, 1.005; 95% CI, 1.002-1.007; P = .002), lower hospital case load (OR for every 1-unit increase in case load, 0.998; 95% CI, 0.998-0.999; P = .001), and year of diagnosis, with less recent procedures more likely to have delay (OR for each additional year, 0.900; 95% CI, 0.884-0.915; P < .001). Patients with surgical treatment within 12 weeks of diagnosis had significantly better overall survival than those with procedures delayed more than 12 weeks (HR, 1.132; 95% CI, 1.064-1.204; P < .001). Using a more precise definition for TTS, this study found that surgical procedures delayed more than 12 weeks were associated with increased risk of recurrence and worse survival. These findings suggest that patients with clinical stage I NSCLC should undergo expeditious treatment within that time frame.