CT-derived body composition associated with lung cancer recurrence after surgery.

OBJECTIVES: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence. METHODS: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence. RESULTS: Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years. CONCLUSIONS: Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.

CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.

BACKGROUND: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. METHODS: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. RESULTS: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. CONCLUSIONS: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

Automated segmentation of five different body tissues on computed tomography using deep learning.

PURPOSE: To develop and validate a computer tool for automatic and simultaneous segmentation of five body tissues depicted on computed tomography (CT) scans: visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. METHODS: A cohort of 100 CT scans acquired on different subjects were collected from The Cancer Imaging Archive-50 whole-body positron emission tomography-CTs, 25 chest, and 25 abdominal. Five different body tissues (i.e., VAT, SAT, IMAT, SM, and bone) were manually annotated. A training-while-annotating strategy was used to improve the annotation efficiency. The 10-fold cross-validation method was used to develop and validate the performance of several convolutional neural networks (CNNs), including UNet, Recurrent Residual UNet (R2Unet), and UNet++. A grid-based three-dimensional patch sampling operation was used to train the CNN models. The CNN models were also trained and tested separately for each body tissue to see if they could achieve a better performance than segmenting them jointly. The paired sample t-test was used to statistically assess the performance differences among the involved CNN models RESULTS: When segmenting the five body tissues simultaneously, the Dice coefficients ranged from 0.826 to 0.840 for VAT, from 0.901 to 0.908 for SAT, from 0.574 to 0.611 for IMAT, from 0.874 to 0.889 for SM, and from 0.870 to 0.884 for bone, which were significantly higher than the Dice coefficients when segmenting the body tissues separately (p < 0.05), namely, from 0.744 to 0.819 for VAT, from 0.856 to 0.896 for SAT, from 0.433 to 0.590 for IMAT, from 0.838 to 0.871 for SM, and from 0.803 to 0.870 for bone. CONCLUSION: There were no significant differences among the CNN models in segmenting body tissues, but jointly segmenting body tissues achieved a better performance than segmenting them separately.

Estimating 3-D whole-body composition from a chest CT scan.

BACKGROUND: Estimating whole-body composition from limited region-computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging. PURPOSE: To investigate if whole-body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only. METHODS: A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole-body positron emission tomography-CT scans at our institution were collected. We used our in-house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field-of-views of the chest CT scans and the whole-body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole-body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten-fold cross-validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R-squared (R2 ) were used as the performance metrics to assess the model performance. RESULTS: The R2 values when estimating volumes of whole-body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively. CONCLUSION: Our algorithm shows promise in its ability to estimate whole-body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar.

Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis

SUMMARY Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis BACKGROUND/AIMS To compare radiological scoring systems, clinical scores, serum C-reactive protein (CRP) levels and the neutrophil-lymphocyte ratio (NLR) for predicting the severity and mortality of acute pancreatitis (AP). MATERIALS AND METHODS Demographic, clinical, and radiographic data from 80 patients with AP were retrospectively evaluated. The harmless acute pancreatitis score (HAPS), systemic inflammatory response syndrome (SIRS), bedside index for severity in acute pancreatitis (BISAP), Ranson score, Balthazar score, modified computed tomography severity index (CTSI), extrapancreatic inflammation on computed tomography (EPIC) score and renal rim grade were recorded. The prognostic performance of radiological and clinical scoring systems, NLR at admission, and serum CRP levels at 48 hours were compared for severity and mortality according to the revised Atlanta Criteria. The data were evaluated by calculating the receiver operator characteristic (ROC) curves and area under the ROC (AUROC). RESULTS Out of 80 patients, 19 (23.8%) had severe AP, and 9 (11.3%) died. The AUROC for the BISAP score was 0.836 (95%CI: 0.735-0.937), with the highest value for severity. With a cut-off of BISAP ≥2, sensitivity and specificity were 68.4% and 78.7%, respectively. The AUROC for NLR was 0.915 (95%CI: 0.790-1), with the highest value for mortality. With a cut-off of NLR >11.91, sensitivity and specificity were 76.5% and 94.1%, respectively. Of all the radiological scoring systems, the EPIC score had the highest AUROC, i.e., 0.773 (95%CI: 0.645-0.900) for severity and 0.851 (95%CI: 0.718-0.983) for mortality, with a cut-off value ≥6. CONCLUSION The BISAP score and NLR might be preferred as early determinants of severity and mortality in AP. The EPIC score might be suggested from the current radiological scoring systems.

RESUMO Comparação dos sistemas de escores radiológicos, escores clínicos razão neutrófilo/linfócito e níveis séricos de proteína C-reativa para determinação da gravidade e mortalidade em casos de pancreatite aguda OBJETIVO Comparar sistemas de escores radiológicos, escores clínicos, os níveis séricos de proteína C-reativa (PCR) e a razão neutrófilo/linfócitos (RNL) como métodos de previsão de gravidade e mortalidade em casos de pancreatite aguda (PA). MATERIAIS E MÉTODOS Dados demográficos, clínicos e radiográficos de 80 pacientes com PA foram avaliados retrospectivamente. Os valores de Harmless Acute Pancreatitis Score (HAPS), Síndrome da Resposta Inflamatória Sistêmica (SIRS), Índice de Gravidade na Pancreatite Aguda à Beira do Leito (BISAP), escore de Ranson, escore de Balthazar, Índice Modificado de Gravidade por Tomografia Computadorizada (CTSI), escore de Inflamação Extrapancreática em Tomografia Computadorizada (EPIC) e grau renal foram registrados. O desempenho prognóstico dos sistemas de escores clínicos e radiológicos e RNL no momento da internação e os níveis séricos de PCR após 48 horas foram comparados quanto à gravidade, de acordo com os critérios de Atlanta revisados e mortalidade. Os dados foram avaliados pelo cálculo das curvas ROC e da área sob a curva ROC (AUROC). RESULTADOS De 80 pacientes, 19 (23,8%) tinham PA grave e 9 (11,3%) morreram. A AUROC para o escore BISAP foi de 0,836 (95%CI: 0.735-0.937), com o valor mais alto de gravidade. Com um valor de corte de BISAP ≥ 2 , a sensibilidade e a especificidade foram de 68,4% e 78,7%, respectivamente. A AUROC para o a RNL foi de 0,915 (95%CI: 0.790-1), com o valor mais alto de mortalidade. Com um valor de corte de RNL > 11,91, a sensibilidade e a especificidade foram de 76,5% e 94,1%, respectivamente. Entre os sistemas de escore radiológico, o EPIC apresentou o maior valor de AUROC, 0,773 (95%CI: 0.645-0.900) para gravidade e 0,851 (95%CI: 0.718-0.983) para mortalidade com um valor de corte ≥6. CONCLUSÃO O escore BISAP e a RNL podem ser preferíveis como determinantes precoces de gravidade e mortalidade na PA. O escore EPIC pode ser sugerido entre os atuais sistemas de escores radiológicos.

How high is the inter-observer reproducibility in the LIRADS reporting system?

PURPOSE: To investigate the reproducibility of LIRADS v2014 and contribute to its widespread use in clinical practice. MATERIAL AND METHODS: This retrospective, single-centre study was conducted between January 2010 and October 2015. A total of 132 patients who had dynamic magnetic resonance imaging (MRI)/computed tomography (CT) images in the Picture Archiving and Communication Systems (PACS) with liver nodule were included in the study, 37 of whom had histopathology results. Five radiologists who participated in the study, interpreted liver nodules independently on different PACS stations according to the LIRADS reporting system and its main parameters. RESULTS: We determined that level of inter-observer agreement in the LR-1, LR-5, and LR-5V categories was higher than in the LR-2, LR-3, and LR-4 categories (κ = 0.522, 0.442, and 0.600 in the LR-1, LR-5, and LR-5V categories, respectively; κ = 0.082, 0.298, and 0.143 in the LR-2, LR-3, and LR-4 categories, respectively). The parameter that we observed to have the highest level of inter-observer agreement was venous thrombus (κ = 0.600). CONCLUSIONS: Our study showed that LIRADS achieves an acceptable inter-observer reproducibility in terms of clinical practice although it is insufficient at intermediate risk levels. We think that the prevalence of its use will be further increased with training related to the subject and the assignment of numerical values that express the probability of malignancy for each category and including the ancillary features in the algorithm according to clearer rules.

Differentiation between Phyllodes Tumors and Fibroadenomas Based on Mammographic Sonographic and MRI Features.

BACKGROUND: This study was performed to compare the mammographic, sonographic, and magnetic resonance imaging (MRI) characteristics of phyllodes tumors and fibroadenomas, which may resemble each other. METHODS: Preoperative mammograms, B-mode and Doppler sonograms, and dynamic breast MRIs of 72 patients with pathologically proven fibroadenomas and 70 patients with pathologically proven phyllodes tumor were evaluated in this retrospective study. Statistical significance was evaluated using chi-square and Fisher's exact tests. Correlations in lesion size among radiological methods were examined by Pearson's correlation analysis. RESULTS: The features that differed on mammogram were size, shape, and margin of the mass. Sonograms showed significant differences in size, shape, margin, echo pattern, and vascularization of the mass. Pearson's correlation analysis showed strong agreement among radiological methods in terms of assessment of size. Tumor size ≥ 3 cm, irregular shape, microlobulated margins, complex internal echo pattern, and hypervascularity were significant findings of phyllodes tumors. Internal cystic areas on MRI were frequently associated with phyllodes tumors. CONCLUSION: Mammographic, sonographic, and MRI findings of fibroadenomas and phyllodes tumors could help radiologists to ascertain imaging-histological concordance and guide clinicians in their decision making regarding adequate follow-up or the necessity of biopsy.