Metastatic Renal Cell Carcinoma to the Testis: A Clinicopathologic Analysis of Five Cases.

The testicular spread of renal cell carcinoma is extremely rare. Five cases of renal cell carcinoma metastatic to the testis are described. The patients ranged from 45 to 81 years of age. Four of the five patients had known renal cell carcinoma. The time intervals between the partial and radical nephrectomies for the primary kidney tumors and the occurrence of testicular metastases ranged from 29 to 34 months. In one patient, the testicular mass was the initial presentation leading to a diagnosis of renal cell carcinoma. There were three ipsilateral metastases, one contralateral metastasis, and one bilateral metastasis. The metastatic deposits ranged in size from 2.0 to 5.7 cm. One case had multiple metastatic tumor nodules. All of the metastatic tumors had clear cell histological features, microscopically concordant with the primary renal cell carcinoma subtype. Three patients died of the disease 17 to 42 months after orchiectomy. One patient is alive with additional metastatic lesions 13 months after orchiectomy. One patient had been free of disease at 87 months after orchiectomy but is now on targeted therapy for an additional metastasis at 93 months after orchiectomy. To date, this report is one of the largest single series of patients with renal cell carcinoma metastatic to the testis, and it has the longest follow-up and survival among all the reported cases.

Clinical and pathological characteristics of bladder cancer in post brachytherapy patients.

The long-term risk of secondary malignancy is a potential late effect of brachytherapy. However, the time interval, anatomic site and histopathology are not well studied. We sought to characterize the bladder cancers that developed following treatment of prostate cancer with brachytherapy. Between 1998 and 2014, 4570 patients were treated with brachytherapy at the BC Cancer Agency. Out of those, 69 patients subsequently developed bladder cancer, some of which could have been radiation induced. Histology slides were reviewed for all cases, and site and pathologic features were recorded. Cases were classified as luminal and basal subtypes based on GATA3 and CK5/6 immunohistochemistry. Bladder neck and trigone were among the common sites of involvement. Pathologic review of cases showed that 68 % were high-grade, 25 % were muscle-invasive, and 20 % showed variant histology, including small cell carcinoma, sarcomatoid carcinoma, squamous cell carcinoma, and adenocarcinoma. A subgroup of cases more likely to be radiation-induced, based on site and time interval, was associated with increased pathologic stage (pT1 or higher) compared to the other cases (70 % vs 34 %, p = 0.01). In conclusion, the majority of bladder cancers following brachytherapy in this cohort were of high grade and low stage at diagnosis, most of them demonstrating luminal immunophenotype. A significant number of variant histologies are seen, each demonstrating a specific immunophenotype.

Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.

Importance: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies. Objectives: To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts. Design, Setting, and Participants: This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019. Main Outcomes and Measures: The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic. Results: On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it was 0.60. Conclusions and Relevance: Results of this study suggest that in prostate cancer classification from histopathologic images, patch-wise CV and single-expert training and evaluation may lead to a biased estimation of classifier's performance. To allow reproducibility and facilitate comparison between automatic classification methods, studies in the field should evaluate their performance using patient-based CV and multiexpert data. Some of these conclusions may be generalizable to other histopathologic applications and to other applications of machine learning in medicine.

Prostatic ductal adenocarcinoma with cribriform architecture has worse prognostic features than non-cribriform-type.

Prostatic ductal adenocarcinoma (PDA) is a rare histologic subtype of prostate cancer characterized by large glands lined with tall columnar pseudostratified epithelium. PDA has several architectural patterns, with papillary and cribriform being the most common. The cribriform pattern of acinar carcinoma has shown to be associated with a worse prognosis in terms of disease progression and disease-specific mortality. However, the significance of cribriform pattern in PDA is unknown. In this study, we sought to compare the adverse pathologic features between cribriform-type and non-cribriform-type PDA, and between PDA and acinar carcinoma with Gleason scores 8-10. We identified PDA cases diagnosed between 2008 and 2018 and 428 radical prostatectomy (RP) specimens containing Gleason 8-10 acinar carcinoma. The slides of all PDA cases were reviewed, and pathologic features were recorded. We found that the vast majority of PDA contained admixed acinar carcinoma, with a median percentage of the ductal component of 50% (range 5-100). 29% of PDA was graded as Grade Group 4 and 35.5% as Grade Group 5. At the time of RP, 45.2% of cases presented as pathologic stage T3a and 29% as T3b. Cribriform-type PDA demonstrated a significantly higher likelihood of extraprostatic extension (84% vs 33.3%, p = 0.01), seminal vesical invasion (36% vs 0%, p = 0.04), lymphovascular invasion (40% vs 0%, p = 0.03) and advanced pathologic stage (84% vs 33.3%, p = 0.01) compared to PDA without cribriform architecture. The proportion of stage ≥pT3 tumors in PDA was similar compared to that in Gleason 8-10 acinar carcinoma (74.2% vs 70.8%, p = 0.68).

Metastatic breast carcinoma to the urinary bladder-a report of 11 cases including a tumor to tumor metastasis.

Metastatic breast carcinoma to the urinary bladder is rare. Eleven cases of metastatic breast carcinoma to the bladder are described in this report, including one case with a tumor to tumor metastasis. The patients ranged from 51 to 83 years of age. The time intervals between the diagnosis of primary breast cancer and the occurrence of bladder metastases ranged from 41 to 336 months. There were seven cases of invasive ductal carcinoma and four cases of invasive lobular carcinoma. In one case, a lobular carcinoma of the breast metastasized to a concurrent squamous cell carcinoma of the bladder. The immunophenotypic status of estrogen receptor and Her2 expression of the metastatic carcinomas were all concordant with the primary tumors. In nine patients with follow-up available, seven patients died of the disease ranging from 1 to 23 months after the diagnosis of the bladder metastasis and two patients were alive at 5 months of follow-up. To date, this report is the largest single series of patients with breast carcinoma metastatic to the bladder. It is the first reported instance of lobular carcinoma of the breast metastasizing to a squamous cell carcinoma of the bladder.

Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.

Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features. A number of novel features are proposed based on intra- and inter-nuclei properties; these features are shown to be among the most important ones for classification. We train our classifiers on 333 tissue microarray (TMA) cores that were sampled from 231 radical prostatectomy patients and annotated in detail by six pathologists for different Gleason grades. We also demonstrate the TMA-trained classifier's performance on additional 230 whole-mount slides of 56 patients, independent of the training dataset, by examining the automatic grading on manually marked lesions and randomly sampled 10% of the benign tissue. For the first time, we incorporate a probabilistic approach for supervised learning by multiple experts to account for the inter-observer grading variability. Through cross-validation experiments, the overall grading agreement of the classifier with the pathologists was found to be an unweighted kappa of 0.51, while the overall agreements between each pathologist and the others ranged from 0.45 to 0.62. These results suggest that our classifier's performance is within the inter-observer grading variability levels across the pathologists in our study, which are also consistent with those reported in the literature.

Fine-needle aspiration of soft tissue myoepithelioma.

Soft tissue myoepithelioma is a rare neoplasm composed of myoepithelial cells. We describe the cytologic features of a soft tissue myoepithelioma arising in the right lower chest wall in a 65-year-old woman. The fine-needle aspiration (FNA) smears showed round to oval, spindle, epithelioid, and plasmacytoid cells in the myxoid background. The nuclei were uniform, round to ovoid, with finely distributed chromatin and eosinophilic or pale cytoplasm, and resembled lobular carcinoma of breast. Ultrasound guided core biopsy showed the tumor cells had bland cytologic features, arranged in small cords, nests, and dissociated single cells, with no glandular differentiation or breast tissue seen. The tumor cells demonstrated immunoreactivity for cytokeratin (AE1/AE3) and glial fibrillary acidic protein, but were negative for estrogen receptor. Fluorescence in situ hybridization demonstrated the EWSR1 rearrangement, confirming the diagnosis of myoepithelioma.