Development of CNS-type Primitive Neuroectodermal Tumor in Metastatic Testicular Mixed Germ Cell Tumor.

A 29-year-old man presenting with a retroperitoneal mass was found to have a testis lesion consistent with mixed germ cell tumor and the RPLND specimen showed teratoma with an area of central nervous system-type primitive neuroectodermal tumor (PNET) not present in the testis. Whether such primitive tumor components represent a de novo tumor component or represent progression from existing neuroepithelial teratomatous elements is unclear. Given the high likelihood of residual tumor and possibility of malignant transformation, post-chemotherapy RPLND remains vital in treating patients with testis cancer. PNET is chemo-resistant and lesions should be resected, without clear evidence for adjuvant chemotherapy

Disparities in prostate cancer treatment associated with population density of the county of residence.

117 Background: We sought to assess disparities in primary treatment of prostate cancer by examining the effect of population density of the county of residence on treatment for clinically localized prostate cancer and quantify variation in primary treatment attributable to county and SEER site. Methods: Participants included 138,226 men with clinically localized prostate cancer who underwent surgery, radiation, or conservative therapy in the Surveillance, Epidemiology, and End Result (SEER) database diagnosed in 2005 through 2008. Mixed effects hierarchical logit models were used to examine the effect of population density of the county of residence on treatment while accounting for the random effects of counties nested within SEER sites. In order to quantify the effect of county and SEER site on individual treatment, the percent of total variance in treatment attributable to county of residence and SEER site was estimated with residual intraclass correlation coefficients. Results: Men in metropolitan counties had 23% higher odds of being treated compared with men in rural counties, controlling for number of urologists per county as well as patient clinical and sociodemographic characteristics. Age, race/ethnicity, PSA at diagnosis, and tumor characteristics were also significant predictors of treatment in the multivariable model. The percent of variation in treatment attributable to the county of residence was consistently higher than that attributable to SEER site. 3% (95% CI: 1.2%-6.2%) of the total variation in treatment was attributable to SEER site, while 6% (95% CI: 4.3%-9.0%) of variation was attributable to county of residence, controlling for clinical and sociodemographic characteristics. This variation at the county and SEER site increased to 4% and 10%, respectively, when specific type of treatment (surgery or radiation) was assessed. Conclusions: Variation in treatment for localized prostate cancer exists for men living in different population dense counties of the country. These findings highlight the need for comparative effectiveness research and health policy initiatives to advance prostate cancer research and reduce disparities in current clinical practice

An Investigation into the Effects of Pre-training Data Distributions for Pathology Report Classification

Pre-trained transformer models have demonstrated success across many natural language processing (NLP) tasks. In applying these models to the clinical domain, a prevailing assumption is that pre-training language models from scratch on large-scale biomedical data results in substantial improvements. We test this assumption with 4 pathology classification tasks on a corpus of 2907 prostate cancer pathology reports. We evaluate 5 transformer pre-trained models that are the same size but differ in pre-training corpora. Specifically, we analyze 3 categories of models: 1)General-domain: BERT and Turing Natural Language Representation (TNLR) models, which use general corpora for pre-training, 2)Mixed-domain: BioBERT which is obtained from BERT by including PubMed abstracts in pre-training and Clinical BioBERT which additionally includes MIMIC-III clinical notes and 3)Domain-specific: PubMedBERT which is pre-trained from scratch on PubMed abstracts. We find the mixed-domain and domain-specific models exhibit faster feature disambiguation during fine-tuning. However, the domain-specific model, PubMedBERT, can overfit to minority classes when presented with class imbalance, a common scenario in pathology report data. At the same time, the mixed-domain models are more resistant to overfitting. Our findings indicate that the use of general natural language and domain-specific corpora in pre-training serve complementary purposes for pathology report classification. The first enables resistance to overfitting when fine-tuning on an imbalanced dataset while the second allows for more accurate modelling of the fine-tuning domain. An expert evaluation is also conducted to reveal common outlier modes of each model. Our results could inform better fine-tuning practices in the clinical domain, to possibly leverage the benefits of mixed-domain models for imbalanced downstream datasets

Using an abdominal phantom to teach urology residents ultrasound-guided percutaneous needle placement

ABSTRACT Introduction: To assess the effect of a hands-on ultrasound training session to teach urologic trainees ultrasound-guided percutaneous needle placement. Materials and methods: University of California, San Francisco (UCSF) urology residents completed a time trial, placing a needle into a phantom model target under ultrasound guidance. Participants were randomized into three educational exposure groups: Group 1's time trial occurred prior to any teaching intervention, group 2's after experiencing a hands-on training module, and group 3's after exposure to both the training module and one-on-one attending feedback. Needle placement speed and accuracy as well as trainees' perceived confidence in utilizing ultrasound were measured. Results: The study cohort consisted of 15 resident trainees. Seven were randomized to group 1, three to group 2, and five to group 3. All residents reported minimal prior ultrasound experience. Their confidence in using ultrasound improved significantly after completing the training module with the most significant improvement seen among junior residents. Time to needle placement was fastest after receiving attending feedback (46.6sec in group 3 vs. 82.7sec in groups 1 and 2, p<0.01). Accuracy also improved with attending feedback, though the number of repositioning attempts did not differ significantly between groups. Conclusions: A hands-on training module and use of an abdominal phantom trainer increased resident confidence and skill in their use of ultrasound to guide percutaneous needle positioning. Attending feedback is critical for improving accuracy in needle guidance toward a target. Ultrasound-guided needle positioning is a teachable skill and can be applicable to multiple urologic procedures