Diagnostic value of machine learning-based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton.

OBJECTIVES: To report the diagnostic performance of machine learning-based CT texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton. METHODS: We retrospectively evaluated 172 patients with multiple myeloma (n = 70) and osteolytic metastatic bone lesions (n = 102) in the peripheral skeleton. Two radiologists individually used two-dimensional manual segmentation to extract texture features from non-contrast CT. In total, 762 radiomic features were extracted. Dimension reduction was performed in three stages: inter-observer agreement analysis, collinearity analysis, and feature selection. Data were randomly divided into training (n = 120) and test (n = 52) groups. Eight machine learning algorithms were used for model development. The primary performance metrics were the area under the receiver operating characteristic curve and accuracy. RESULTS: In total, 476 of the 762 texture features demonstrated excellent interobserver agreement. The number of features was reduced to 22 after excluding those with strong collinearity. Of these features, six were included in the machine learning algorithms using the wrapper-based classifier-specific technique. When all eight machine learning algorithms were considered for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton, the area under the receiver operating characteristic curve and accuracy were 0.776-0.932 and 78.8-92.3%, respectively. The k-nearest neighbors model performed the best, with the area under the receiver operating characteristic curve and accuracy values of 0.902 and 92.3%, respectively. CONCLUSION: Machine learning-based CT texture analysis is a promising method for discriminating multiple myeloma from osteolytic metastatic bone lesions.

Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors.

PURPOSE: Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions that are classified as three different forms as benign phyllodes tumor (BPT), borderline phyllodes tumor (BoPT), and malignant phyllodes tumor (MPT). Conventional radiologic methods make only a limited contribution to exact diagnosis, and texture analysis data increase the diagnostic performance. In this study, we aimed to evaluate the contribution of texture analysis of US images (TAUI) of PTs in order to discriminate between BPTs and BoPTs-MPTs. METHODS: The number of patients was 63 (41 BPTs, 12 BoPTs, and 10 MPTs). Patients were divided into two groups (Group 1-BPT, Group 2-BoPT/MPT). TAUI with LIFEx software was performed retrospectively. An independent machine learning approach, MATLAB R2020a (Math- Works, Natick, Massachusetts) was used with the dataset with p < 0.004. Two machine learning approaches were used to build prediction models for differentiating between Group 1 and Group 2. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance of statistically significant texture data between phyllodes subgroups. RESULTS: In TAUI, 10 statistically significant second order texture values were identified as significant factors capable of differentiating among the two groups (p < 0.05). Both of the models of our dataset make a diagnostic contribution to the discrimination between BopTs-MPTs and BPTs. CONCLUSION: In PTs, US is the main diagnostic method. Adding machine learning-based TAUI to conventional US findings can provide optimal diagnosis, thereby helping to choose the correct surgical method. Consequently, decreased local recurrence rates can be achieved.

Sonoelastography findings of myofibroblastoma.

Myofibrolastoma of the breast (MFB) is a rare and benign stromal tumor. Although MFB is a benign entity, in radiologic evaluation, there is no specific diagnostic feature. Conventional breast imaging findings have been published before. Sonoelastography has been used as an imaging method that allows us to evaluate tissue stiffness in vivo and increases the specificity of B-mode ultrasonography in the discrimination of benign-malignant lesions. In this case report, it was shown that MFB is of high stiffness in shear wave elastography (SWE) evaluation. SWE findings of MFB, which is a benign lesion, have been described for the first time in the literature.

GNRI And Conut Scores: Simple Predictors of Sarcopenia in Metastatic Colorectal Cancer Patients.

OBJECTIVE: To evaluate the correlation between sarcopenia and inflammation- and nutrition-based markers in metastatic colorectal cancer (mCRC) patients. MATERIALS AND METHODS: Age, body mass index (BMI), neutrophil/lymphocyte ratio (NLR), modified Glasgow prognostic score (mGPS), prognostic nutrition index (PNI), cachexia index (CIn), skeletal muscle index (SMI), controlling nutritional status (CONUT) score, and geriatric nutritional risk index (GNRI) were evaluated in 185 patients. Ideal cut-off values for the GNRI score were determined with the ROC curve analysis, and the patients were divided into two groups as low and high GNRI. Sarcopenia was diagnosed using CT scanning, the gold standard method. Univariate and multivariate Cox proportional hazard analyses were done based on the above-listed parameters to assess the correlation between sarcopenia and changes in immuno-nutrition and inflammatory response. Kaplan-Meier analysis was also done to evaluate survival. RESULTS: Univariate analysis of the 185 patients based on the EGWSOP 2018 threshold values showed correlation between the presence of sarcopenia and male gender, diagnosed colon cancer, history of metastasectomy, BMI < 24, high mGPS score, PNI score ≥ 45, high CONUT score, and low GNRI score (p < 0.05). In multivariate analysis, low GNRI (HR: 2.40; 95% CI: 1.03-5.544; p = 0.040), and high-CONUT scores (HR: 2.01; 95% CI: 1.06-3.73; p = 0.029) were identified as independent prognostic factors for the presence of sarcopenia. CONCLUSION: GNRI and CONUT scores are elementary and practical predictors for sarcopenia, a condition which is associated with poor outcomes in mCRC patients.

[Evaluation of solid pulmonary nodules in patients with bladder cancer with computed tomography based texture analysis method: Is it possible to differentiate metastatic - non-metastatic nodules?] / Bilgisayarli tomografi tabanli doku analizi yöntemi ile mesane kanserli hastalarda solid pulmoner nodüllerin degerlendirilmesi: Metastatik - metastatik olmayan nodül ayrimini yapmak mümkün mü?

INTRODUCTION: Solid Pulmonary Nodule (SPN) is defined as parenchymal radiopacity smaller than 3 cm in diameter. Evaluating the metastatic nature of the SPNs detected in the thorax computed tomography (TCT) examination for staging purposes in cancer patients becomes a fundamental problem for the physician. Invasive procedures, additional imaging or follow-up imaging, are often used to differentiate metastatic and non-metastatic nodules. In this study, we aimed to distinguish SPNs detected in patients diagnosed with bladder cancer (BC) as metastatic and non-metastatic nodules by texture analysis. MATERIALS AND METHODS: TCT images of patients diagnosed with BC in our hospital from January 2007 until December 2017 were retrospectively evaluated. A total of 46 patients with SPN, including metastatic (n= 19) and non-metastatic (n= 27), were included in the study. Short axis diameter, long-axis diameter, nodule volume and volume histogram values of the nodules were obtained. Chisquare test was used to evaluate dependent variables, and the Mann-Whitney U test was used to evaluate independent variables. ROC curves of the obtained data were plotted. Statistically, the significant p-value was determined as less than 0.05. RESULT: A significant difference was found between SPN long axis, short axis and volume values. In the volumetric histogram analysis, the maximum density value and the mean density value were found to be statistically significant. When the average of the highest densities in the volume histogram data was evaluated, the area under the curve value was 0.702 (95% CI, 519-854). The metastatic nodule could be distinguished with a sensitivity of 88% and a specificity of 70% when the volume histogram has the maximum density threshold of 50 HU. CONCLUSIONS: In this study, we concluded that SPN detected on CT images can be distinguished as metastatic and non-metastatic nodules using texture analysis method without invasive procedures.

Three cases of immunoglobulin G4-related respiratory disease with uncommon imaging findings.

BACKGROUND: Immunoglobulin G4-related disease (IgG4-RD) is a rare multisystemic idiopathic fibroinflammatory disorder. The rare form of IgG4-RD with isolated thorax involvement is called immunoglobulin G4-related respiratory disease (IgG4-RRD). IgG4-RRD, which is reported in a limited number of cases in the literature, can be categorized into four types on the prevalent chest computed tomography (CCT) findings: solid nodular, round-shaped ground-glass opacity, alveolar interstitial, and bronchovascular. Solid nodular form of IgG4-RRD with mass-like lesions is sporadic and described in the literature with a small number of case reports. OBJECTIVES/METHODS: We aim to present the radiologic, pathologic, and clinical findings of three cases of IgG4-RRD mimicking lung cancer. RESULTS: In all three patients, IgG4-RRD occurred with mass-like lesions in the thorax. In case-1 and 2, CCT showed multiple, nodular lesions and multiple mediastinal lymph nodes. On positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro- D-glucose integrated with computed tomography (18F-FDG PET/CT), the masses showed increased 18F-FDG uptake in case-2 and 3. The gold standard histopathological verification for IgG4-RRD was provided for all cases. CONCLUSIONS: IgG4-RD is an immune-mediated condition comprised of a collection of disorders that share particular pathologic, radiologic, serologic, and clinical features. Isolated IgG4-RRD is rarely seen and is available in the literature as case reports. IgG4-RRD, which can make lung involvement in different patterns, rarely appears with mass-like lesions. Still, IgG4-RRD must be considered in the differential diagnosis of mass lesions detected in CCT. Laboratory, radiological, and histopathological findings of the disease should be evaluated together for an accurate diagnosis.

Retroperitoneal extensive free air bubbles due to decompression illness.

Decompression illness (DCI) is a rare condition caused by air bubbles that arise because of a rapid decrease in ambient pressure. These air bubbles exert both physical and chemical effects associated with a range of findings from asymptomatic clinical presentation to death. In the literature, changes in consciousness, severe musculoskeletal and abdominal pain, respiratory distress, and skin changes have been described. The diagnosis of DCI is difficult, but anamnesis and physical examination are helpful. Radiologic evaluation is useful for determining possible complications in patients with severe disease and excluding other acute pathologies. In computed tomography (CT) images of patients diagnosed with DCI, air bubbles in the portal venous system, iliac and mesenteric veins, the vena cava inferior (VCI), and the cerebral and spinal arteries have been described before. Herein, we present the clinical and CT findings of two cases of DCI with extensive intra-abdominal free bubbles evident on abdominal CT.

Computed tomography characteristics of multiple myeloma and other osteolytic metastatic bone lesions.

BACKGROUND: Differentiation of multiple myeloma (MM) from osteolytic metastatic (OM) bone lesions may be critical in patients with lytic bone lesions but can be challenging for radiologists. PURPOSE: To determine whether computed tomography (CT) can be used to distinguish between MM and other OM bone lesions. MATERIAL AND METHODS: In this retrospective study, 320 lesions of 207 patients diagnosed with MM or OM, based on biopsy or clinical examination, were evaluated. Eight qualitative features were evaluated by two radiologists blinded to the diagnoses. The chi-square and Fisher exact tests, and logistic regression analysis, were used to evaluate the relationships between the CT findings and diagnoses. RESULTS: High-density areas were more common in OM than MM lesions (85.2% and 19%, P < 0.001), as were perilesional sclerosis (38.9% vs. 13.2%, P < 0.001), heterogeneity (on non-contrast CT images, 60% vs. 19.1%, P < 0.001; on contrast enhanced CT images, 80.6% vs. 28.2%, P < 0.001), and ill-defined margins (34.6% vs. 9.1%, P < 0.001). Similarly, OM lesions showed high-density areas more than MM in evaluation of skeletal system subgroups (vertebrae, 93.8% vs. 29.8%, P < 0.0001; thoracic cage bones, 69.6% vs. 19.2%, P < 0.001; pelvic bones and sacrum, 84.8% vs. 7.7%, P < 0.001; peripheral skeletal bones, 81.5% vs. 8.3%, P < 0.001). Logistic regression analysis revealed that the presence of a high-density area in the lesion increased the probability of a metastasis 25.88-fold (R2 = 0.516, P < 0.001). CONCLUSION: MM and OM lesions can be differentiated by CT; OM lesions exhibit high- density areas.

Evaluation of Elastographic Features in Complex Fibroadenomas With Radiologic-Pathologic Correlation.

Fibroadenoma is the most common benign tumor of the breast, and complex fibroadenoma (CFA) is one of its variants. Of the fibroadenomas, 22% are CFAs, and in women with CFAs, the malignancy development is found to be higher than in women with noncomplex fibroadenomas. Although there is an increased risk of malignancy with CFAs, the imaging findings of CFAs are fundamentally similar to those of other variants of fibroadenomas. In the literature, B-mode ultrasound features of CFAs were reported in detail. To our knowledge, there is no study that has specifically described the elastographic findings of CFAs. This article aims to illustrate the elastographic features of CFAs and to correlate radiologic and histopathologic findings of different cases.

Elastography findings of encapsulated solid papillary carcinoma of breast in a man.

Encapsulated solid papillary carcinoma (ESPC) is one of the malignant papillary lesions and classified it as ductal carcinoma in situ. Radiologic features of ESPC have been reported in the literature many times. However, to our best knowledge US elastography findings of ESPC have not been reported in the published literature yet. In this case report, we present US elastography findings of ESPC.

Sex determination with morphological characteristics of the skull by using 3D modeling techniques in computerized tomography.

Sex determination is a major area of investigation in forensic anthropology. As technology has advanced, imaging methods such as computed tomography and magnetic resonance imaging are being investigated as alternatives to conventional forensic anthropological research techniques. This study aimed to investigate the suitability of three-dimensional (3D) modeling of volumetric cranial computed tomography (CCT) images for sex estimation from skull morphology. In this study, CCT angiography images from the Department of Radiology 2017 archives were used retrospectively, and 3D images were obtained after the reconstruction of 85 cases of CCT images. The sex-dependent morphological characteristics of the skull were evaluated by three blinded observers and scored on a scale of 1-5 points according to the "Standards for Data Collection from Human Skeletal Remains". The accurate sex estimation rates of the first, second and third observers were 91.8, 92.9 and 92.9%, respectively. The rate of accurate sex estimation for males was 98-100%, while this rate varied between 83.3-86.1% for females. Consistency in sex estimation between the three observers was 83.5%, with a Kappa value of 0.763 (z = 12.2; p = 0.0001*). The glabella was the most effective morphological trait used to estimate sex. The results of this study show that sex can be estimated from morphological features in volume-rendered CCT 3D images. Thus, sex can be estimated by digital images without the need for maceration processes, and the transfer of digital data in place of physical material will make it possible to gain expert opinions in forensic anthropology.