CT Scans Of Patients Research Articles

Admission chest CT scan of intensive care patients with interstitial lung disease: Unveiling its limited predictive value through visual and automated analyses in a retrospective study (ILDICTO)

BackgroundClinical course prediction of patients with interstitial lung disease (ILD) admitted to the intensive care unit (ICU) for acute respiratory failure (ARF) can be challenging. This study aimed to characterize the prognostic value of admission chest CT-scan in this situation. MethodsWe retrospectively included ILD patients admitted to a French ICU for acute respiratory failure requiring oxygen. Patients with lymphangitis carcinomatosis and ANCA vasculitis were excluded. We analyzed every admission chest CT-scan using two different approaches: a visual analysis (grading the extent of traction bronchiectasis, ground glass and honeycomb) and an automated analysis (grading the extent of ground glass and consolidation with a dedicated software). The primary outcome was ICU mortality. ResultsBetween January 2014 and October 2020, 81 patients presented an acute respiratory failure with ILD on the admission chest CT-scan. In univariate analysis, only the main pulmonary artery diameter differed between patients who survived and those who died in ICU (30 vs 32 mm, p = 0.021). In multivariate analysis, none of the radiological funding was associated with ICU mortality. Visual and automated analyses did not yield different results, with a strong correlation between the two methods. However, the identification of an UIP pattern (and the presence of honeycomb) was associated with a poorer response to corticosteroid therapy. ConclusionOur study showed that the extent of radiological findings and the severity of fibrosis indices on admission chest CT scans of ILD patients admitted to the ICU for ARF were not associated with subsequent deterioration.

Value of projectional imaging relative to cross-sectional imaging to assess catheter tip position in the superior vena cava: Evaluation of reader variability.

The assessment of correct positioning of central venous catheters (CVC) is of major concern to avoid complications.Vascular access associations have established the cavo-atrial junction (CAJ) as the most appropriate CVC tip location. Among the different chest X-ray (CXR) landmarks proposed for assessing tip position relative to the CAJ, only the pericardial reflection lies in the same plane as the vascular structures assessed. Although extensively used, CXR has been criticized for its difficulty in estimating catheter tip location.We aimed to evaluate the observer variability to determine tip positioning on CXR. CT imaging was used as a gold standard for tip identification. 107 CT scans of patients wearing port access catheter devices realized at the XXX University Hospital between January and December 2021 were retrospectively analyzed. Distance from tip to cavo-atrial junction (DCAJ) was measured on topogram projectional imaging (PJ) and axial cross-sectional imaging (CS) by 2 × 2 observers (within and between evaluations). Observational statistics were reported using a paired t-test, repeatability coefficients (RC), and the intraclass correlation coefficient (ICC), and they were displayed comprehensively using Bland-Altman plots. All ICC were >0.9, indicating excellent reliability. The mean difference between observers comparing CS and PJ was 0.13 ± 0.80 cm (P = 0.10) with outer 95% confidence limits of 1.92 cm and -2.17 cm and an RC of 1.79 cm. CXR tip-position reading remains an accurate method for determining CVC localization. However, the assessment variability on CXR is plus or minus 2 cm and should be considered. Chest X-ray (CXR) for the assessment of tip position is subject to intra-individual and inter-individual variation. On CT, the variability is solely linked to determining the cavoatrial junction (CAJ) and represented about 1 cm. On CXR, there is a twofold challenge: the correct determination of the CAJ and the accurate identification of the catheter tip, leading to a greater variability of 2 cm.In clinical practice, while considering the 3 cm anatomical zone around the CAJ acceptable, operators should be aware of the 2 cm variability resulting from CXR assessment. To account for this variability and avoid the risk of positioning the tip beyond 3 cm from the CAJ (theoretically up to plus or minus 5 cm away from the CAJ), operators should reduce the CXR-based acceptable zone to 1 cm around the CAJ. This change may impact up to thirty percent of procedures.

Radiological indicators of chronicity in patients with acute symptomatic pulmonary embolism treated with ultrasound-assisted catheter-directed thrombolysis: a study of 180 consecutive cases

Abstract Background Radiological signs of chronic thrombi are present in up to 20% of patients that present acutely with pulmonary embolism (PE). Ultrasound-Assisted Catheter-Directed Thrombolysis (USAT) can swiftly reverse right ventricular dysfunction in patients with acute PE. Purpose To investigate the frequency of radiological signs of chronic PE and pre-existing chronic thromboembolic pulmonary hypertension in patients treated with USAT and their impact on early and long-term hemodynamic and clinical outcomes. Methods The index CT scans of patients receiving USAT for acute intermediate-high and high-risk PE at a tertiary academic institution (2017-2022) were adjudicated by three expert radiologists blinded for treatment scheme and outcomes. Changes in invasively-measured mean pulmonary artery pressure (mPAP) at 24 hours post USAT, presence of post-PE impairment (PPEI), and CTEPH diagnoses after at least 3 months were studied. A total of 15 radiological signs of chronicity were assessed, including 6 major criteria that have been previously validated to raise the suspicion of pre-existing CTEPH. In this study, acute-on-chronic PE was defined when at least 3 of the 6 validated radiological criteria for chronicity were present together with acute findings. Pre-existing CTEPH was defined based on the results of follow-up (vs. index) CT scan and on the anatomical consistency of pathology specimens after pulmonary endarterectomy. PPEI was defined by the presence of residual respiratory symptoms and right ventricular dysfunction during follow-up. Results Of 180 consecutive patients (median age 65 years, 61% men, 17% high-risk PE), 31 had at least 3 of 6 radiological criteria at the time of acute presentation; Table 1. The median modified Miller Score was 24 points and baseline mPAP was 35 mmHg in both groups. The absolute mPAP reduction after USAT was 11 (Q1-Q3: 5-17) mmHg in patients with <3 and 10 (Q1-Q3: 5, 17) mmHg in those with ≥3 criteria; Table 2. Three (1.7%) patients were diagnosed with CTEPH after a median of 3 months and underwent pulmonary endarterectomy: in all 3 patients, CTEPH was pre-existing. At least 3 of the 6 previously validated criteria were present in all CTEPH patients at index CT. In the 3 patients with acute PE on pre-existing CTEPH, the median baseline mPAP was much higher (median 53 mmHg) than in the remaining patient group and the absolute reduction after USAT was 9 mmHg, corresponding to a relative reduction of 19%. During follow-up, PPEI was more frequent in patients with <3 signs (n=4, 2.7%) than in those with ≥3 signs (n=4, 12.9%). Conclusion(s) The evaluation of six radiological parameters in initial CT scans successfully identified all cases of pre-existing CTEPH in patients presenting with acute PE. However, these parameters did not prove effective in determining which patients would benefit from USAT in terms of mPAP reduction.Baseline characteristicsHemodynamic and clinical outcomes

Are muscle and fat loss predictive of clinical events in pancreatic cancer? The importance of precision metrics

BackgroundMuscle and fat loss from cancer may have prognostic significance. Skeletal muscle and fat areas measured at L3 on a CT scan correlate with body muscle and fat mass. We wished to know if reduced skeletal muscle area or fat on diagnostic CT scans or changes from initial CT scans in patients with pancreatic cancer who died in 2018 and 2019 predicted mortality. MethodeEectronic records of 112 patients with locally advanced or metastatic pancreatic cancer were used to extract stage, age, gender, comorbidities, weight, and height at the time of the first CT scan. Survival (in days) was defined from the first CT scan to the death date. Patients had at least one CT scan of the abdomen. I. Two trained medical students read scans independently using TeraRecon software (Durham, NC). Results were averaged, and the differences determined precision. Interclass correlation coefficient (ICC), coefficient of variation, and least significant change determined the precision between readers. Independent prognostic modeling included age and BMI. ResultsAn evaluable sample of 104 with an average age of 67, 56 were male. Nearly half had a TNM Stage of IV (45%). The average Charlson Comorbidity index is 7.2. In those undergoing repeat scans, most were in the timeframe of 60-120 days. Changes in visceral fat in men in the unadjusted Cox proportional hazard model and reduced skeletal muscle area in the age-adjusted model of men predicted mortality. In contrast, myosteatosis in women marginally predicted improved survival. ICC's precision between readers was adequate but by least significant change would have missed subtle, clinically important changes. DiscussionMuscle loss during chemotherapy in men predicted mortality in men but not women. Precision is an important metric when measuring body composition. ConclusionMuscle loss in men during chemotherapy of pancreatic cancer predicts mortality.

Phase 2 trial of PSMA PET CT versus planar bone scan and CT in prostate cancer patients progressing while on androgen deprivation therapy

For prostate cancer patients who experience biochemical progression during androgen deprivation therapy (ADT), prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) has not been prospectively compared to planar bone scan plus CT. This was a single-arm, head-to-head, prospective phase II trial (NCT04928820) designed to enroll 102 men with prostate cancer who experienced biochemical progression (rising prostate-specific antigen [PSA] ≥ 1 ng/mL) during ADT. All patients received 68Ga-PSMA-11 PET/CT and 99mTc-MDP planar bone scans. Each scan was interpreted by three central independent readers. The primary endpoint was the per-patient bone metastasis detection rate of PSMA PET/CT versus planar bone scan and CT. Secondary endpoints compared the number of bone metastases detected per patient and the inter-reader agreement of each imaging modality. Twenty-two men were enrolled between July 2021 and June 2022. Due to slow accrual following approval of PSMA PET radiotracers in the U.S. and a lack of a statistical signal between the two imaging modalities on interim analysis, this trial was closed early on October 2022. Median PSA was 8.5 ng/mL (interquartile range: 1.6–77.6). There was 100% agreement between the two scans. Six patients (27%) had negative findings and 16 patients (73%) had positive findings on both scans. PSMA PET/CT and bone scan plus CT detected an equal number of bone lesions for 14 patients (64%), PSMA PET/CT detected more bone lesions for six patients (27%), and bone scan plus CT detected more bone lesions for two patients (9.1%) (p = 0.092). The inter-reader agreement rates of PSMA PET/CT and bone scan plus CT were 96% and 82%, respectively (p = 0.25). In men with biochemical progression during ADT, 68Ga-PSMA-11 PET/CT and 99mTc-MDP planar bone scan plus CT had identical bone metastasis detection rates. Bone scan plus CT can continue to serve as a cost-effective and readily accessible restaging modality in patients with biochemical progression. ClinicalTrials.gov NCT04928820. Registered 16/06/2021.

Activation of a Soft Robotic Left Ventricular Phantom Embedded in a Closed-Loop Cardiovascular Simulator: A Computational and Experimental Analysis.

Cardiovascular simulators are used in the preclinical testing phase of medical devices. Their reliability increases the more they resemble clinically relevant scenarios. In this study, a physiologically actuated soft robotic left ventricle (SRLV) embedded in a hybrid (in silico- in vitro) simulator of the cardiovascular system is presented, along with its experimental and computational analysis. A SRLV phantom, developed from a patient's CT scan using polyvinyl alcohol (PVA), is embedded in a hybrid cardiovascular simulator. We present an activation method in which the hydraulic pressure external ( ) to the SRLV is continuously adapted to regulate the left ventricular volume ( ), considering the geometry and material behavior of the SRLV and the left ventricular pressure ( ). This activation method is verified using a finite element (FE) model of the SRLV and validated in the hybrid simulator. Different hemodynamic profiles are presented to test the flexibility of the method. Both the FE model and hybrid simulator could represent the desired in silico data ( , ) with the implemented activation method, with deviations below 8.09% in the FE model and mainly < 10% errors in the hybrid simulator. Only two measurements out of 32 exceeded the 10% threshold due to simulator setup limitations. The activation method effectively allows to represent various pressure-volume loops, as verified numerically, and validated experimentally in the hybrid simulator. This work presents a high-fidelity platform designed to simulate cardiovascular conditions, offering a robust foundation for future testing of cardiovascular medical devices under physiological conditions.

The Association Between Hounsfield Units and Mechanical Failure in ASD Patients.

Retrospective Cohort Study. Low bone mineral density (BMD) is a known risk factor for revision surgery in patients with adult spinal deformity (ASD). Hounsfield units (HUs) on CT scans have been suggested as a proxy for assessing BMD. This study aimed to determine HUs in the lumbar region and their association with mechanical failure in patients undergoing ASD surgery. We included ASD patients undergoing surgery from 2010-2020 with minimum 2-year follow-up. We excluded patients without preoperative CT scans, or a CT scan more than 1year before surgery. Mechanical failure was defined as proximal junctional failure, pseudarthrosis, or implant failure requiring revision surgery. On preoperative CT scans, HUs were measured on 3 axial slices on each vertebra from L1-L5 and, if available, at UIV and UIV + 1. We included 170 patients, mean age 63 (±12) years, with 108 (64%) females, and 13 [IQR 10-16] instrumented levels. Mechanical failure occurred in 27% (n = 46) of patients at 2-year follow-up. Mean lumbar HUs were 146 (±51) in the mechanical failure group and 135 (±52) in those without revision (P = .232). Area under the curve was 0.58 (95% CI: 0.48-0.68), corresponding to no to low discriminatory power in predicting mechanical failure using lumbar HUs. Univariate logistic regression revealed no significant difference between mechanical failure and lumbar HUs (OR = 1.00, 95% CI: 1.00-1.01, P = .239). We found no association between mechanical failure and HUs on preoperative CT scans in ASD patients. Thus, we cannot recommend using HUs to predict mechanical failure in these patients.

Predictors of Chest CT Total Severity Score InCovid-19 Infected Patients In Rwanda

Background: Over 5.54 million COVID-19 deaths occurred worldwide by the January 2021 and COVID-19 pneumonia remains the commonest cause of death. The current study was conducted to investigate predictors of total severity score in patients with COVID-19 pneumonia who underwent chest CT and in turn be used in setting with limited resource to predict TSS as well as prognosis Materials and Methods: The current study retrospectively evaluated the CT findings in COVID-19 patients with clinical severity using a 25-point visual quantitative assessment and total severity score predictors were determined. A sample of 384 COVID-19 patients was used and Statistical analysis was done using SPSS 25.0. Results: The findings of the current study indicate that age (p &lt; 0.001), vaccination status (p = 0.006), and disease status (p &lt; 0.001) are significant predictors of the total severity score on chest CT scans in patients with COVID-19 pneumonia. Specifically, as age increases, the odds of having a higher total severity score also increase. Conversely, individuals who are vaccinated tend to have lower severity scores, suggesting that COVID-19 vaccination is associated with a reduced likelihood of experiencing severe disease. Additionally, patients with severe COVID-19 pneumonia are more likely to exhibit higher severity scores on Chest CT. Conclusion: Chest CT scan total severity score can be predicted with age, immunization status and disease status classified as mild, moderate, severe and critical covid-19 pneumonia and this score can be used to predict clinical outcome. Recommendation: A large multicenter study can be used to validate current study findings and these clinical predictors can be used in resource limited settings where CT scan accessibility is not ensured.

S1615 Correlation of Tumoral and Peri-Tumoral Findings Observed on CT Scan and Endoscopic Ultrasonography in Patients With Gastrointestinal Stromal Tumors

S1615 Correlation of Tumoral and Peri-Tumoral Findings Observed on CT Scan and Endoscopic Ultrasonography in Patients With Gastrointestinal Stromal Tumors

Factors influencing hematoma expansion in delayed brain CT scans of patients with traumatic Epidural Hematoma.

Epidural hematoma (EDH) is a type of intracranial hematoma commonly observed in trauma patients. This research aims to evaluate the factors contributing to the expansion of traumatic epidural hematoma (EDH). This retrospective cohort study examined traumatic patients with EDH admitted to Taleqani Hospital, a prominent Western Trauma Center in Iran, from 2018 to 2023. Patients underwent an initial CT scan, and non-surgical patients received a delayed CT scan approximately 5 hours after the initial scan. Data analysis was performed using SPSS version 25 software. The study included 274 brain trauma patients with epidural hematoma. Among these patients, 142 (51.8%) did not undergo surgery, and 121 (85.2%) of the non-surgical patients were male. Motor vehicle accidents (MVAs) were the primary cause of EDH in 127 (46.4%) patients. The mean (±standard deviation) initial hematoma size was 8.86 (±9.71), and the mean (±standard deviation) delayed CT scan size was 8.12 (±8.10). In crude linear regression Two variables, namely Hematoma volume in the initial CT scan (P less than 0.001) and Mixed Density (P=0.007), were found to significantly impact the increase in hematoma size in delayed CT scans of non-surgical patients. But in the adjusted linear regression model, only the Hematoma volume in primary CT scan, was significant (P less 0.001). The R Square values were 0.72. The identification of key variables influencing hematoma volume in delayed CT scans has the potential to guide more effective interventions, thereby improving patient outcomes and reducing trauma-related mortality.

Abstract A145: Race and sex disparities in lung cancer screening and evaluation of alternative identifiers in patients’ electronic medical record to better identify lung cancer screening candidates

Abstract Introduction: The US Preventive Services Task Force (USPSTF) recommends lung cancer screening with a low dose CT scan in patients who are 50 – 80 years old with at least a 20 pack-year smoking history and currently smoke or quit in the last 15 years. Yet, only 5.8 percent of eligible Americans were screened in 2022. We investigated demographic screening patterns and evaluated for alternative identifiers in patients’ electronic medical record (EMR) to identify lung cancer screening candidates at a large and diverse cancer center in Los Angeles. Methods: We examined 607 patients with non-small cell lung cancer (NSCLC) treated at the University of Southern California Norris Comprehensive Care Center or Los Angeles General Medical Center from 2009-2023. EMR data included age of diagnosis, sex, race/ethnicity, smoking history, and stage at diagnosis; for patients with unknown number of pack years (n=25) they counted as not qualified for screening. Identifiers investigated were International Classification of Disease, Tenth Revision (ICD-10) code for chronic obstructive pulmonary disease (COPD), prescription of inhaled steroids, and documented lung cancer history. Fisher’s exact test was used to compare proportion of qualified screeners among groups. Results: 35% (215/607) of patients would have qualified for lung cancer screening according to the USPSTF guidelines, including a greater proportion of males 52% (164/313) versus females 17% (51/293) (p&amp;lt;0.0001). In males, Non-Hispanic Whites (NHW) 59% (55/93) had a greater proportion that would qualify compared to Hispanics 41% (31/75) (p=0.0295). In females, NHW 34% (30/89) had the highest proportion of qualifiers while Asians 6% (6/104) and Hispanics 9% (6/66) had the lowest proportion of qualifiers (p&amp;lt;0.0001). 65% (215/332) of smokers qualified for screening; males had a higher percentage who qualified for screening versus females (164/234, 70% vs. 51/98, 52%, p=0.0024). By race, NHW male smokers had the highest proportion of qualifiers 77% (55/71) while Hispanic women had the lowest proportion of qualifiers 50% (6/12). 27% (89/332) of patients were found to have an ICD-10 code for COPD, including 18% (21/117) had an ICD-10 code for COPD who did not qualify. 19% (63/332) smokers received a prescription for inhaled steroids, including 15% (18/117) of them who didn’t qualify for screening. 12% had a family history of lung cancer (39/332), while 9% (10/117) of these patients didn’t qualify for screening. Conclusion: There were significant race and sex screening disparities among known NSCLC patients including under 10% of Asian and Hispanic females. There were about 10-15% known lung cancer patients who would not have qualified for screening found to have an ICD-10 code for COPD, an inhaled steroids prescription, or documented family history. These alternative identifiers may serve as identifiers for lung cancer screening candidates in the EMR to help address race and sex disparities in screening. Future directions should include simulation of these identifiers in larger patient datasets. Citation Format: Darin Poei, Aubree Mades, Iris Yao, Nick Ahn, Ajay Prasad, Jennifer Tsui, Jacob S Thomas, Jorge J Nieva, Robert Hsu. Race and sex disparities in lung cancer screening and evaluation of alternative identifiers in patients’ electronic medical record to better identify lung cancer screening candidates [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A145.

Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke.

Rapid diagnosis of acute ischemic stroke (AIS) is critical to achieve positive outcomes and prognosis. This study aimed to construct a model to automatically identify the infarct core based on non-contrast-enhanced CT images, especially for small infarcts. The baseline CT scans of AIS patients, who had DWI scans obtained within less than 2 h apart, were included in this retrospective study. A modified Target-based deep learning model of YOLOv5 was developed to detect infarctions on CT. Randomly selected CT images were used for testing and evaluated by neuroradiologists and the model, using the DWI as a reference standard. Intraclass correlation coefficient (ICC) and weighted kappa were calculated to assess the agreement. The paired chi-square test was used to compare the diagnostic efficacy of physician groups and automated models in subregions. p < 0.05 was considered statistically significant. Five hundred and eighty four AIS patients were enrolled in total, finally 275 cases were eligible. Modified YOLOv5 perform better with increased precision (0.82), recall (0.81) and mean average precision (0.79) than original YOLOv5. Model showed higher consistency to the DWI-ASPECTS scores (ICC = 0.669, κ = 0.447) than neuroradiologists (ICC = 0.452, κ = 0.247). The sensitivity (75.86% vs. 63.79%), specificity (98.87% vs. 95.02%), and accuracy (96.20% vs. 91.40%) were better than neuroradiologists. Automatic model had better diagnostic efficacy than physician diagnosis in the M6 region (p = 0.039). The deep learning model was able to detect small infarct core on CT images more accurately. It provided the infarct portion and extent, which is valuable in assessing the severity of disease and guiding treatment procedures.

Evaluating mechanical benefit of wedge osteotomies in endoscopic surgery for sagittal synostosis using patient-specific 3D-printed models.

Endoscopically assisted sagittal strip craniotomy with subsequent cranial orthosis is a frequently used surgical approach for non-syndromic sagittal synostosis. Originally, this technique involved a wide sagittal strip craniectomy with bilateral wedge osteotomies. More recent studies suggest omitting wedge osteotomies, achieving similar outcomes. The controversy surrounding wedge osteotomies and our efforts to refine our technique led us to create models and evaluate the mechanical impact of wedge osteotomies. We conducted a 3D-print study involving preoperative CT scans of non-syndromic scaphocephaly patients undergoing minimally invasive-assisted remodelation (MEAR) surgery. The sagittal strip collected during surgery underwent thickness measurement, along with a 3-point bending test. These results were used to determine printing parameters for accurately replicating the skull model. Model testing simulated gravitational forces during the postoperative course and assessed lateral expansion under various wedge osteotomy conditions. The median sagittal strip thickness was 2.00mm (range 1.35-3.46mm) and significantly positively correlated (p = 0.037) with the median force (21.05 N) of the 3-point bending test. Model testing involving 40 models demonstrated that biparietal wedge osteotomies significantly reduced the force required for lateral bone shift, with a trend up to 5-cm-long cuts (p = 0.007). Additional cuts beyond this length or adding the occipital cut did not provide further significant advantage (p = 0.1643; p = 9.6381). Biparietal wedge osteotomies reduce the force needed for lateral expansion, provide circumstances for accelerated head shape correction, and potentially reduce the duration of cranial orthosis therapy.

Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is a poor-prognosis disease. Owing to the recent availability of new therapeutic options, there is a need to better assess prognosis. The initial clinical response could represent a useful parameter. We proposed a transfer learning approach to predict an initial treatment response starting from baseline CT scans of patients with advanced/unresectable MPM undergoing first-line systemic therapy. The therapeutic response has been assessed according to the mRECIST criteria by CT scan at baseline and after two to three treatment cycles. We used three slices of baseline CT scan as input to the pre-trained convolutional neural network as a radiomic feature extractor. We identified a feature subset through a double feature selection procedure to train a binary SVM classifier to discriminate responders (partial response) from non-responders (stable or disease progression). The performance of the prediction classifiers was evaluated with an 80:20 hold-out validation scheme. We have evaluated how the developed model was robust to variations in the slices selected by the radiologist. In our dataset, 25 patients showed an initial partial response, whereas 13 patients showed progressive or stable disease. On the independent test, the proposed model achieved a median AUC and accuracy of 86.67% and 87.50%, respectively. The proposed model has shown high performance even by varying the reference slices. Novel tools could help to improve the prognostic assessment of patients with MPM and to better identify subgroups of patients with different therapeutic responsiveness.

A humeral intracondylar fissure elevates maximum principal bone strain in the humeral condyle and lateral epicondylar crest in French Bulldogs.

To investigate whether a humeral intracondylar fissure (HIF) alters bone strain in the French Bulldog humerus, we developed a quantitative CT-based 3-D finite element (FE) model for virtual mechanical testing. We hypothesized that higher strains would be seen in the intracondylar region and lateral epicondylar crest if there was a HIF. Patient CT scans from 3 (n = 3) French Bulldogs were selected. Dog 1 had a closed distal physis and no HIF. Dog 2 had an open distal humeral physis but no HIF. Dog 3 had an open distal physis and a HIF. A 3-D FE model was built for FE analysis, and pressure was applied to the humerus over the region that contacts the radial head. The maximum principal bone strain patterns differed in each of the models. A path of strain concentration mimicking the typical pattern of a lateral condylar fracture was only found in dog 3. Maximum principal strain exceeded 1% in parts of the lateral epicondylar crest in all 3 dogs. We developed a patient-specific, quantitative CT-based 3-D FE model for virtual mechanical testing. We accepted our hypothesis. Strain concentration occurred in the intracondylar region and along the lateral epicondylar crest only when a HIF was present. The presence of a HIF in French Bulldogs elevates maximum principal bone strain in this region and alters its path in an FE model, which suggests an increased risk of a lateral humeral condylar fracture.

Association between muscle mass and overall survival among colorectal cancer patients at tertiary cancer center in the Middle East

Recent reports have shown that pre-treatment low muscle mass may lead to poorer outcomes for cancer patients. We explored the correlation between Visceral Adipose Tissue (VAT), Subcutaneous Adipose Tissue (SAT), and Muscle Mass (MM) as measured by CT scans, and overall survival (OS) following diagnosis of colorectal cancer (CRC). We conducted a retrospective review of medical records and CT scans of patients diagnosed with CRC between 2007 and 2018. Demographics, pathology, and clinical parameters were collected. Using Image-J software, we measured VAT, SAT, and MM. Survival rates were analyzed using Kaplan–Meier curves, and prognostic factors were assessed using multivariate Cox regression. Analysis included 408 patients with a mean age of 56.9 years and a median follow-up of 93.3 months. Colon and rectum/rectosigmoid colon cancers were equally distributed. The 5-year OS rate was 67.8%. There was no significant difference in OS rates based on SAT or VAT. However, higher MM was associated with a improved 5-year OS rate. Factors such as age, stage, grade, and surgery were also associated to OS rates. These findings suggest that higher muscle mass may lead to better outcomes for CRC patients, highlighting the potential impact of exercise and nutritional interventions on patient outcomes.

Computer tomography-based assessment of perivascular adipose tissue in patients with abdominal aortic aneurysms

This retrospective study investigates perivascular adipose tissue (PVAT) alterations in CT as a marker of inflammation in patients with abdominal aortic aneurysms (AAA). 100 abdominal CT scans of patients with abdominal aortic aneurysms and 100 age and sex matched controls without underlying aortic disease were included. Artificial Intelligence (AI) assisted segmentation of the aorta and the surrounding adipose tissue was performed. Adipose tissue density was measured in Hounsfield units (HU) close (2-5mm, HUclose) and distant (10-12mm, HUdistant) to the aortic wall. To investigate alterations in adipose tissue density close to the aorta (HUclose) as a potential marker of inflammation, we calculated the difference HUΔ = HUclose-HUdistant and the fat attenuation ratio HUratio = HUclose/HUdistant as normalized attenuation measures. These two markers were compared i) inter-individually between AAA patients and controls and ii) intra-individually between the aneurysmal and non-aneurysmal segments in AAA patients. Since most AAAs are generally observed infrarenal, the aneurysmal section of the AAA patients was compared with the infrarenal section of the aorta of the control patients. In inter-individual comparisons, higher HUΔ and a lower HUratio were observed (aneurysmal: 8.9 ± 5.1 HU vs. control: 6.9 ± 4.8 HU, p-value = 0.006; aneurysmal: 89.8 ± 5.7% vs. control: 92.1 ± 5.5% p-value = 0.004). In intra-individual comparisons, higher HUΔ and lower HUratio were observed (aneurysmal: 8.9 ± 5.1 HU vs. non-aneurysmal: 5.5 ± 4.1 HU, p-value < 0.001; aneurysmal: 89.8 ± 5.7% vs. non-aneurysmal 93.3 ± 4.9%, p-value < 0.001). The results indicate PVAT density alterations in AAA patients. This motivates further research to establish non-invasive imaging markers for vascular and perivascular inflammation in AAA.

AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.

Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ2 test and Kruskal-Wallis rank sum test were used for statistical analyses. Results A total of 24 401 participants (median age, 61 years [IQR, 57-65 years]; 14 468 male) were included. In 3880 of 24 401 participants (16%), 4283 extrapulmonary significant incidental findings were reported. During the 10-year follow-up, 3389 of 24 401 participants (14%) died. CAC had the highest feature importance for predicting the three study end points. The 10-year ACM model demonstrated the best AUC performance (0.72; per-year mortality of 2.6% above and 0.8% below the risk threshold), followed by 2-year ACM (0.71; per-year mortality of 1.13% above and 0.3% below the risk threshold) and prediction of extrapulmonary significant incidental findings (0.70; probability of occurrence of 25.4% above and 9.6% below the threshold). Conclusion A fully automated AI model indicated extrapulmonary structures at risk on chest CT scans and predicted ACM with explanations. ClinicalTrials.gov Identifier: NCT00047385 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yanagawa and Hata in this issue.

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study

ObjectivesAlthough neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response...

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age [±SD], 60 years ± 11; 143 female), was tested on two internal datasets (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the F1/Dice coefficient, SDs, and 95% CIs, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved F1/Dice scores of 85.5% ± 6.1 (95% CI: 83.1, 87.8) and 82.6% ± 15.3 (95% CI: 76.4, 88.7), with median volume estimation errors of 19.6% (IQR, 13.2%-29.0%) and 5.3% (IQR: 2.4%-9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a F1/Dice score of 83.0% ± 10.7 (95% CI: 79.8, 86.3) and median volume estimation error of 9.7% (IQR, 4.5%-15.1%). The model showed strong agreement with expert assessments, with r2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in patients with cirrhosis and those with ovarian cancer, in concordance with expert radiologist assessments. Keywords: Abdomen/GI, Cirrhosis, Deep Learning, Segmentation Supplemental material is available for this article. © RSNA, 2024 See also commentary by Aisen and Rodrigues in this issue.