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Multiphasic enhancement patterns of small renal masses (?4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma.

ABSTRACT: To analyze the enhancement patterns of small renal masses (SRMs) during 4-phase computed tomography (CT) imaging to predict histology.One-hundred consecutive patients with SRMs and 4-phase preoperative CT imaging, who underwent extirpative surgery with a pathologic diagnosis of renal cell carcinoma (RCC), angiomyolipoma (AML), or oncocytoma, were identified from a single institution. An expert radiologist, blinded to histologic results, retrospectively recorded tumor size, RENAL (radius, exophytic/endophytic properties of the tumor, nearness of tumor deepest portion to the collecting system or sinus, anterior/posterior descriptor, and the location relative to polar lines) nephrometry score, tumor attenuation, and the renal cortex on all 4 acquisitions (precontrast, corticomedullary, nephrogenic, and delayed density).Pathologic diagnoses included 48 clear-cell RCCs (ccRCCs), 22 papillary RCCs, 10 chromophobe RCCs, 13 oncocytomas, and 7 AMLs. There was no significant difference in median tumor size (P = .8), nephrometry score (P = .98), or anatomic location (P >.2) among histologies. Significant differences were noted in peak enhancement (P <.001) and phase-specific enhancement (P <.007) by histology. Papillary RCCs demonstrated a distinct enhancement pattern, with a peak Hounsfield unit (HU) of 56, and greatest enhancement during the NG and delayed phases. The highest peak HU were demonstrated by ccRCC (117 HU) and oncocytoma (125 HU); ccRCC more often peaked in the corticomedullary phase, whereas oncocytoma peaked in the nephrogenic phase.In a series of patients with SRMs undergoing 4-phase CT, tumor histologies demonstrated distinct enhancement patterns. Thus, preoperative 4-phase CT imaging may provide useful information regarding pathologic diagnosis in patients undergoing extirpative surgery.

SUBMITTER: Pierorazio PM

PROVIDER: S-EPMC4648275 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Multiphasic enhancement patterns of small renal masses (≤4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma.

Pierorazio Phillip M PM Hyams Elias S ES Tsai Salina S Feng Zhaoyong Z Trock Bruce J BJ Mullins Jeffrey K JK Johnson Pamela T PT Fishman Elliot K EK Allaf Mohamad E ME

Urology 20130417 6

<h4>Objective</h4>To analyze the enhancement patterns of small renal masses (SRMs) during 4-phase computed tomography (CT) imaging to predict histology.<h4>Methods</h4>One-hundred consecutive patients with SRMs and 4-phase preoperative CT imaging, who underwent extirpative surgery with a pathologic diagnosis of renal cell carcinoma (RCC), angiomyolipoma (AML), or oncocytoma, were identified from a single institution. An expert radiologist, blinded to histologic results, retrospectively recorded ...[more]

PMID: 23601445

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Project description:ObjectiveTo evaluate the feasibility and effectivity of deep learning (DL) plus three-dimensional (3D) printing in the management of giant sporadic renal angiomyolipoma (RAML).MethodsThe medical records of patients with giant (>15 cm) RAML were retrospectively reviewed from January 2011 to December 2020. 3D visualized and printed kidney models were performed by DL algorithms and 3D printing technology, respectively. Patient demographics and intra- and postoperative outcomes were compared between those with 3D-assisted surgery (3D group) or routine ones (control group).ResultsAmong 372 sporadic RAML patients, 31 with giant ones were eligible for analysis. The median age was 40.6 (18-70) years old, and the median tumor size was 18.2 (15-28) cm. Seventeen of 31 (54.8%) had a surgical kidney removal. Overall, 11 underwent 3D-assisted surgeries and 20 underwent routine ones. A significant higher success rate of partial nephrectomy (PN) was noted in the 3D group (72.7% vs. 30.0%). Patients in the 3D group presented a lower reduction in renal function but experienced a longer operation time, a greater estimated blood loss, and a higher postoperative morbidity. Subgroup analysis was conducted between patients undergoing PN with or without 3D assistance. Despite no significant difference, patients with 3D-assisted PN had a slightly larger tumor size and higher nephrectomy score, possibly contributing to a relatively higher rate of complications. However, 3D-assisted PN lead to a shorter warm ischemia time and a lower renal function loss without significant difference. Another subgroup analysis between patients under 3D-assisted PN or 3D-assisted RN showed no statistically significant difference. However, the nearness of tumor to the second branch of renal artery was relatively shorter in 3D-assisted PN subgroup than that in 3D-assisted RN subgroup, and the difference between them was close to significant.Conclusions3D visualized and printed kidney models appear to be additional tools to assist operational management and avoid a high rate of kidney removal for giant sporadic RAMLs.

Project description:ObjectiveThis study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and malignant parotid gland (PG) tumors via multicentre cohorts.Materials and methodsA cohort of 87 PG tumor patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single tumor region of interest (ROI) of each patient on each phase of CT images. Imaging features were extracted from plain CT and contrast-enhanced CT (CECT) images. After dimensionality reduction, a radiomics signature was constructed. A combination model was constructed by incorporating the rad-score and CT radiological features. An independent group of 38 patients from hospital #2 was used to validate the prediction models. The model performances were evaluated by receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the models. The radiomics signature model was constructed and the rad-score was calculated based on selected imaging features from plain CT and CECT images.ResultsAnalysis of variance and multivariable logistic regression analysis showed that location, lymph node metastases, and rad-score were independent predictors of tumor malignant status. The ROC curves showed that the accuracy of the support vector machine (SVM)-based prediction model, radiomics signature, location and lymph node status in the training set was 0.854, 0.772, 0.679, and 0.632, respectively; specificity was 0.869, 0.878, 0.734, and 0.773; and sensitivity was 0.731, 0.808, 0.723, and 0.742. In the test set, the accuracy was 0.835, 0.771, 0.653, and 0.608, respectively; the specificity was 0.741, 0.889, 0.852, and 0.812; and the sensitivity was 0.818, 0.790, 0.731, and 0.716.ConclusionsThe combination model based on the radiomics signature and CT radiological features is capable of evaluating the malignancy of PG tumors and can help clinicians guide clinical tumor management.

Dataset Information

Multiphasic enhancement patterns of small renal masses (?4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma.

Publications

Multiphasic enhancement patterns of small renal masses (≤4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma.

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