The prostatic adenocarcinoma with mucinous features: A review of the literature with three case reports.

Mucinous adenocarcinoma of the prostate is one of the rare variants of the prostatic carcinoma, and its incidence among all prostatic carcinomas is reported to be 0.3% in the literature. If the tumor variant containing extracellular mucin in <25% of the resected tumor mass, the histology is defined as adenocarcinoma with mucinous features. The mucinous adenocarcinoma of the prostate displays similar prognostic features with the classic adenocarcinoma. In this study, the treatment and surveillance processes of our three patients with prostatic adenocarcinoma with mucinous features were presented along with a literature review.

Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.

RATIONALE AND OBJECTIVES: This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis. MATERIALS AND METHODS: Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest. RESULTS: The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively. CONCLUSION: ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.

CD10, TDAG51, CK20, AR, INSM1, and Nestin Expression in the Differential Diagnosis of Trichoblastoma and Basal Cell Carcinoma.

BACKGROUND: Trichoblastoma (TB) and basal cell carcinoma (BCC) are 2 different neoplasms composed of basaloid cells and have overlapping histopathological features. We compared the immunoexpression of CD10, T-cell death-associated gene 51 (TDAG51), cytokeratin 20 (CK20), androgen receptor (AR), insulinoma-associated protein 1 (INSM1), and nestin for the differential diagnosis of these tumors. MATERIALS AND METHODS: We assessed a total of 27 BCC and 27 TB cases, including 4 TB lesions in nevus sebaceous and 3 malignant TB lesions for CD10, TDAG51, CK20, AR, INSM1, and nestin expression. RESULTS: Staining for CK20, TDAG51, INSM1, and stromal CD10 was significantly more common in TB cases than in BCC cases ( P < .001). Epithelial CD10 and AR staining was significantly more common in BCC cases than in TB cases ( P < .001). The difference between the groups for nestin staining was not significant ( P > .05). Stromal CD10 staining was the most sensitive marker (96.3%) and INSM1 the least sensitive (55.6%) marker for TB. TDAG51 showed 100% specificity for TB. A larger number of CK20 positive cells was found in the cases associated with nevus sebaceous than in the other TBs. CONCLUSION: All the selected markers except nestin were useful for the differential diagnosis between TB and BCC. CD10 and TDAG51 were more useful than the other markers. The use of CK20 could be preferred in nevus sebaceous lesions. INSM1 was less effective in highlighting Merkel cells within the lesion than CK20.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

OBJECTIVE: To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs). MATERIALS AND METHODS: This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics. RESULTS: Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively. CONCLUSIONS: The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs. KEY POINTS: • Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.

OBJECTIVE: To develop externally validated, reproducible, and generalizable models for distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning-based quantitative computed tomography (CT) texture analysis (qCT-TA). MATERIALS AND METHODS: Sixty-eight RCCs were included in this retrospective study for model development and internal validation. Another 26 RCCs were included from public databases (The Cancer Genome Atlas-TCGA) for independent external validation. Following image preparation steps (reconstruction, resampling, normalization, and discretization), 275 texture features were extracted from unenhanced and corticomedullary phase CT images. Feature selection was firstly done with reproducibility analysis by three radiologists, and; then, with a wrapper-based classifier-specific algorithm. A nested cross-validation was performed for feature selection and model optimization. Base classifiers were the artificial neural network (ANN) and support vector machine (SVM). Base classifiers were also combined with three additional algorithms to improve generalizability performance. Classifications were done with the following groups: (i), non-clear cell RCC (non-cc-RCC) versus clear cell RCC (cc-RCC) and (ii), cc-RCC versus papillary cell RCC (pc-RCC) versus chromophobe cell RCC (chc-RCC). Main performance metric for comparisons was the Matthews correlation coefficient (MCC). RESULTS: Number of the reproducible features is smaller for the unenhanced images (93 out of 275) compared to the corticomedullary phase images (232 out of 275). Overall performance metrics of the machine learning-based qCT-TA derived from corticomedullary phase images were better than those of unenhanced images. Using corticomedullary phase images, ANN with adaptive boosting algorithm performed best for discrimination of non-cc-RCCs from cc-RCCs (MCC = 0.728) with an external validation accuracy, sensitivity, and specificity of 84.6%, 69.2%, and 100%, respectively. On the other hand, the performance of the machine learning-based qCT-TA is rather poor for distinguishing three major subtypes. The SVM with bagging algorithm performed best for discrimination of pc-RCC from other RCC subtypes (MCC = 0.804) with an external validation accuracy, sensitivity, and specificity of 69.2%, 71.4%, and 100%, respectively. CONCLUSIONS: Machine learning-based qCT-TA can distinguish non-cc-RCCs from cc-RCCs with a satisfying performance. On the other hand, the performance of the method for distinguishing three major subtypes is rather poor. Corticomedullary phase CT images provide much more valuable texture parameters than unenhanced images.

Atypical spindle cell/pleomorphic lipomatous tumor: A clinicopathologic, immunohistochemical, and molecular study of 20 cases.

Lipomatous tumors accompanied by spindle cell component are not frequently encountered, and there are still problems regarding their differential diagnosis, nature, and nomenclature. To contribute to ongoing efforts, we present the clinical, histologic, and immunohistochemical characteristics of 20 cases of spindle cell lipomatous tumors with atypical features that may also be called atypical spindle cell/pleomorphic lipomatous tumors. Of the patients, 13 were men and 7 were women with an average age of 57.5 years. The most commonly affected site was the extremities. Twelve tumors arose in the subcutaneous tissue, while eight cases were located in the deep soft tissues. Tumor margins were often ill-defined with invasion into the surrounding tissues. Microscopic examination revealed a wide spectrum of histologic features. All cases consisted of poorly marginated proliferation of mildly atypical spindle cells set in a fibrous or myxoid stroma with a variable amount of adipocytic component showing variation in adipocyte size and scattered nuclear atypia and frequent univacuolated or multivacuolated lipoblasts. Tumor cellularity and the relative proportion of the components were highly variable. One tumor showed morphologic features evocative of dedifferentiation and another one exhibited histological features resembling pleomorphic liposarcoma. None of the patients had recurrence or metastasis at follow-up.

Inverted (hobnail) high-grade prostatic intraepithelial neoplasia and invasive inverted pattern.

High-grade prostatic intraepithelial neoplasia (HGPIN) is considered to be an important precursor for prostatic adenocarcinoma. The present study aimed to investigate the histological features of the uncommon inverted (hobnail) pattern of HGPIN in transrectal ultrasonographic (TRUS) prostatic needle biopsies from 13 cases. These 13 diagnosed cases of inverted HGPIN were identified out of a total of 2,034 TRUS biopsies (0.63%), obtained from patients suspected to have prostate cancer. The hobnail pattern is comprised of secretory cell nuclei, which are histologically localized at the luminal surface of the prostate gland, rather than the periphery, and exhibit reverse polarity. Histological examinations were performed and the results demonstrated that 5 of the 13 cases exhibited pure inverted histology, while HGPIN was observed to be histologically associated with other patterns in the remaining 8 patients. In addition, an association with adenocarcinoma was identified in 7 of the 13 cases. All 7 carcinomas accompanied by inverted HGPIN were conventional acinar adenocarcinoma cases; of note, for these 7 cases, the Gleason score was 7 for each. One acinar adenocarcinoma case accompanying inverted HGPIN demonstrated hobnail characteristics in large areas of the invasive component. It was observed that nuclei were proliferated in the invasive cribriform glands, which was comparable to that of inverted HGPIN, and were located on the cytoplasmic luminal surface; a similar morphology was also observed in individual glands. In conclusion, the results of the present study suggested that the hobnail HGPIN pattern may be of diagnostic importance due to its high association with adenocarcinoma and the high Gleason scores in the accompanying carcinomas.

Paratesticular sarcomas: A report of seven cases.

Primary tumors of the paratesticular region are rare, with paratesticular sarcomas constituting a major proportion of these tumors, particularly in the elderly. The paratesticular region consists of mesothelial, various epithelial and mesenchymal cells and may therefore give rise to a number of tumors with various behaviors. Defining the association between the paratesticular mass and the testicle, and differentiation between benign and malignant masses using radiology is challenging, therefore the mass is usually considered to be malignant and radical orchiectomy with high ligation is performed. The present study reports the cases of seven patients with tumors of the paratesticular region and presents the clinical and significant histological features of the tumors. In total, two patients suffered from dedifferentiated liposarcoma (DDLS), two exhibited leiomyosarcoma, two exhibited low-grade fibromyxoid sarcoma and one case of undifferentiated pleomorphic sarcoma was identified. Radical orchiectomy with high ligation was performed in five cases; simple orchiectomy was performed in one case and excisional biopsy was performed in the remaining case. A leiomyosarcomatous and epithelial membrane antigen (EMA) positive whorl pattern was observed during microscopy in the two DDLS cases. Additionally, one of the low-grade fibromyxoid sarcoma patients exhibited pleomorphism and mitosis in focal areas. To the best of our knowledge, the present study is the second time low-grade fibromyxoid sarcoma cases with paratesticular localization have been reported in the literature. Of the seven cases, four patients succumbed to the disease, one patient is living with the disorder and the two cases of DDLS are living without the disease. Paratesticular sarcomas are often aggressive and a multidisciplinary approach is required for the diagnosis and treatment of these tumors.

Foamy gland carcinoma in core needle biopsies of the prostate: clinicopathologic and immunohistochemical study of 56 cases.

Foamy gland carcinoma is a subtype of acinar adenocarcinoma characterized by foamy appearance, large cytoplasm, pyknotic nuclei, inconspicuous nucleoli and infiltrative pattern. In this study, we investigated the histological features and the incidence of foamy gland carcinoma. We compared foamy gland carcinoma with acinar adenocarcinoma according to age, prostate-specific antigen value, Gleason score, peripheral nerve invasion and accompanying high-grade prostatic intraepithelial neoplasia. Besides, we investigated the diagnostic value of immunohistochemical markers in foamy gland carcinoma. A total of 863 TRUS-guided prostate needle core biopsies performed at our hospital pathology clinic between January 1, 2010, and December 31, 2011, were examined, 251 of these were diagnosed acinar type adenocarcinoma. Conventional acinar type adenocarcinoma was present in 195 (78%) cases, and foamy gland carcinoma, in 56 cases (22%). We found that 11 (19%) of the 56 foamy gland carcinoma cases were pure and 45 (81%) cases were mixed with conventional acinar type adenocarcinoma. Single-core localization was present in 7 of 14 pure foamy gland carcinomas, and the number of cases with a Gleason score of 7 and above was 21 (37%). No statistically significant difference was found between foamy gland carcinoma and conventional acinar type adenocarcinoma in terms of age, Gleason score, high-grade prostatic intraepithelial neoplasia, and prostate-specific antigen values. Peripheral nerve invasion was found to be statistically significantly more common in foamy gland carcinoma compared to acinar type adenocarcinoma (P<.05). The staining percentage of immunohistochemical markers in foamy gland carcinoma was 90.1% for p63, 90.6% for 34Beta12 and 90.6% for AMACR.