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Thyroid cancer incidence increased with the greatest change in adults aged ≥65 years. To determine the relationship between area-level use of imaging and thyroid cancer incidence over time. Longitudinal imaging patterns in Medicare patients aged ≥65 years residing in Surveillance, Epidemiology, and End Results (SEER) regions were assessed in relationship to differentiated thyroid cancer diagnosis in patients aged ≥65 years included in SEER-Medicare. Linear mixed-effects modeling was used to determine factors associated with thyroid cancer incidence over time. Multivariable logistic regression was used to determine patient characteristics associated with receipt of thyroid ultrasound as initial imaging. Thyroid cancer incidence. Between 2002 and 2013, thyroid ultrasound use as initial imaging increased (P < 0.001). Controlling for time and demographics, use of thyroid ultrasound was associated with thyroid cancer incidence (P < 0.001). Findings persisted when cohort was restricted to papillary thyroid cancer (P < 0.001), localized papillary thyroid cancer (P = 0.004), and localized papillary thyroid cancer with tumor size ≤1 cm (P = 0.01). Based on our model, from 2003 to 2013, at least 6594 patients aged ≥65 years were diagnosed with thyroid cancer in the United States due to increased use of thyroid ultrasound. Thyroid ultrasound as initial imaging was associated with female sex and comorbidities. Greater thyroid ultrasound use led to increased diagnosis of low-risk thyroid cancer, emphasizing the need to reduce harms through reduction in inappropriate ultrasound use and adoption of nodule risk stratification tools.

ObjectivesTo develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.MethodsThis institutional review board–approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC: 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same datasets and the obtained results were compared with those acquired from slice-wise algorithms.ResultsFor differentiation of RCC versus benign solid masses, the semi-automated majority voting-based CNN algorithm achieved accuracy, precision, and recall of 83.75%, 89.05%, and 91.73% using 160 test cases, respectively. Fully automated pipeline yielded accuracy, precision, and recall of 77.36%, 85.92%, and 87.22% on the same test cases, respectively. 3D CNN reported accuracy, precision, and recall of 79.24%, 90.32%, and 84.21% using 160 test cases, respectively.ConclusionsA semi-automated majority voting CNN-based methodology enabled accurate classification of RCC from benign neoplasms among solid renal masses on CECT.Key Points• Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images.• A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images.• Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%).

Purpose To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma. Materials and methods Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data. Results One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 ( p  < 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 ( p  < 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively. Conclusion Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.

The detection of a renal mass is a relatively frequent occurrence in the daily practice of any Radiology Department. The diagnostic approaches depend on whether the lesion is cystic or solid. Cystic lesions can be managed using the Bosniak classification, while management of solid lesions depends on whether the lesion is well-defined or infiltrative. The approach to well-defined lesions focuses mainly on the differentiation between renal cancer and benign tumors such as angiomyolipoma (AML) and oncocytoma. Differential diagnosis of infiltrative lesions is wider, including primary and secondary malignancies and inflammatory disease, and knowledge of the patient history is essential. Radiologists may establish a possible differential diagnosis based on the imaging features of the renal masses and the clinical history. The aim of this review is to present the contribution of the different imaging techniques and image guided biopsies in the diagnostic management of cystic and solid renal lesions.

Abstract Context A subset of thyroid carcinomas expresses an oncogenic paired box 8 (PAX8) and peroxisome proliferator activated receptor γ (PPARγ) fusion protein (PPFP). The PPARγ/PPFP ligand pioglitazone is highly therapeutic in a transgenic mouse model of PPFP thyroid carcinoma, but whether pioglitazone is therapeutic in patients with PPFP thyroid carcinoma is unknown. Case Description Tumor blocks from 40 patients with progressive thyroid cancer despite standard-of-care therapy were screened for PPFP, and the tumor from only one patient (2.5%) was positive. The patient had a 6.0-cm acetabular soft tissue metastasis from Hürthle cell carcinoma that caused severe pain on weight bearing and had a serum thyroglobulin level of 1974 ng/mL. After 24 weeks of therapy with pioglitazone, the metastatic lesion was 3.9 cm, the thyroglobulin level was 49.4 ng/mL, and the patient was pain-free. Thirteen months after discontinuation of pioglitazone, the metastatic lesion was 3.6 cm, the thyroglobulin level was 4.7 ng/mL, and the patient remained pain-free. Conclusions Pioglitazone may be therapeutic in patients with PPFP thyroid cancer. However, thyroid cancers that are progressive despite standard-of-care therapy appear to only rarely express PPFP. A patient with PPFP-positive thyroid cancer had a major clinical improvement, as well as radiological and biochemical improvements, after therapy with pioglitazone was begun.

RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Fourteen patients (6 RO and 8 ChRCC) were included in the prospective study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice.

Adrenocortical tumours are rare in children and account for only 0.3%–0.4% of all neoplasms in childhood. They present with variable signs and symptoms, depending on the type of hormonal hypersecretion. The majority of the adrenocortical tumours in children are functional (90%) and malignant (88%). Here, we describe a functional plurihormonal oncocytic adrenal cortical adenoma in a young girl, that mimicked a malignant adrenal lesion, clinically as well as on imaging and biochemical features. This report bears the objective of being aware of the atypical biochemical as well as imaging characteristics of oncocytic adrenal tumours.

ObjectivesTo perform a systematic review on apparent diffusion coefficient (ADC) values of renal tumor subtypes and meta-analysis on the diagnostic performance of ADC for differentiation of localized clear cell renal cell carcinoma (ccRCC) from other renal tumor types.MethodsMedline, Embase, and the Cochrane Library databases were searched for studies published until May 1, 2019, that reported ADC values of renal tumors. Methodological quality was evaluated. For the meta-analysis on diagnostic test accuracy of ADC for differentiation of ccRCC from other renal lesions, we applied a bivariate random-effects model and compared two subgroups of ADC measurement with vs. without cystic and necrotic areas.ResultsWe included 48 studies (2588 lesions) in the systematic review and 13 studies (1126 lesions) in the meta-analysis. There was no significant difference in ADC of renal parenchyma using b values of 0–800 vs. 0–1000 (p = 0.08). ADC measured on selected portions (sADC) excluding cystic and necrotic areas differed significantly from whole-lesion ADC (wADC) (p = 0.002). Compared to ccRCC, minimal-fat angiomyolipoma, papillary RCC, and chromophobe RCC showed significantly lower sADC while oncocytoma exhibited higher sADC. Summary estimates of sensitivity and specificity to differentiate ccRCC from other tumors were 80% (95% CI, 0.76–0.88) and 78% (95% CI, 0.64–0.89), respectively, for sADC and 77% (95% CI, 0.59–0.90) and 77% (95% CI, 0.69–0.86) for wADC. sADC offered a higher area under the receiver operating characteristic curve than wADC (0.852 vs. 0.785, p = 0.02).ConclusionsADC values of kidney tumors that exclude cystic or necrotic areas more accurately differentiate ccRCC from other renal tumor types than whole-lesion ADC values.Key Points• Selective ADC of renal tumors, excluding cystic and necrotic areas, provides better discriminatory ability than whole-lesion ADC to differentiate clear cell RCC from other renal lesions, with area under the receiver operating characteristic curve (AUC) of 0.852 vs. 0.785, respectively (p = 0.02).• Selective ADC of renal masses provides moderate sensitivity and specificity of 80% and 78%, respectively, for differentiation of clear cell renal cell carcinoma (RCC) from papillary RCC, chromophobe RCC, oncocytoma, and minimal-fat angiomyolipoma.• Selective ADC excluding cystic and necrotic areas are preferable to whole-lesion ADC as an additional tool to multiphasic MRI to differentiate clear cell RCC from other renal lesions whether the highest b value is 800 or 1000.

Objectives To assess the feasibility of intraparotid facial nerve (VIIn) tractographic reconstructions in estimating the presence of a contact between the VIIn and the tumour, in patients requiring surgical resection of parotid tumours. Methods Patients underwent MR scans with VIIn tractography calculated with the constrained spherical deconvolution model. The parameters of the diffusion sequence were: b-value of 1000 s/mm 2 ; 32 directions; voxel size: 2 mm isotropic; scan time: 9’31’. The potential contacts between VIIn branches and tumours were estimated with different initial fractional anisotropy (iFA) cut-offs compared to surgical data. Surgeons were blinded to the tractography reconstructions and identified both nerves and contact with tumours using nerve stimulation and reference photographs. Results Twenty-six patients were included in this study and the mean patient age was 55.2 years. Surgical direct assessment of VIIn allowed identifying 0.1 as the iFA threshold with the best sensitivity to detect tumour contact. In all patients with successful VIIn identification by tractography, surgeons confirmed nerve courses as well as lesion location in parotid glands. Mean VIIn branch FA values were significantly lower in cases with tumour contact (t-test; p  ≤ 0.01). Conclusions This study showed the feasibility of intraparotid VIIn tractography to identify nerve contact with parotid tumours. Key points • Diffusion imaging is an efficient method for highlighting the intraparotid VIIn . • Visualization of the VIIn may help to better manage patients before surgery . • We bring new insights to future trials for patients with VIIn dysfunction . • We aimed to provide radio - anatomical references for further studies .

PurposesLow-grade oncocytic tumor (LOT) is a rare renal tumor that has emerged from the spectrum of eosinophilic/oncocytic renal tumors and poses a diagnostic challenge due to its similarity to chromophobe renal cell carcinoma (CHRCC) and renal oncocytoma (RO). The imaging features of this novel tumor entity have not yet been clearly described. The purpose of this study was to describe the imaging features of LOT with radiologic-pathologic correlation.MethodsWe conducted a retrospective observational study involving two expert centers. We identified 12 pathologically proven LOT with preoperative imaging available, including at least computed tomography (CT) or magnetic resonance imaging (MRI), from the past 12 years. Three experienced radiologists performed the imaging analysis independently.ResultsAll tumors presented well-defined borders. Nine of the 12 LOT exhibited an early peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape. Three showed a homogeneous contrast enhancement. Macroscopic fat and calcifications were not observed in any of the tumors.ConclusionEarly peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape suggests a LOT diagnosis. Further analyses involving larger studies are needed to fully confirm these imaging characteristics. To date, a percutaneous biopsy should be performed before considering management.