Accounting for early job turnover in recent pediatric surgery fellowship graduates: An American Pediatric Surgical Association Membership and Credentials Committee study

PURPOSE: Employment opportunities for graduating pediatric surgeons vary from year to year. Significant turnover among new employees indicates fellowship graduates may be unsophisticated in choosing job opportunities which will ultimately be satisfactory for themselves and their families. The purpose of this study was to assess what career, life, and social factors contributed to the turnover rates among pediatric surgeons in their first employment position. METHODS: American Pediatric Surgical Association members who completed fellowship training between 2011 and 2016 were surveyed voluntarily. Only those who completed training in a pediatric surgery fellowship sanctioned by the American Board of Surgery and whose first employment involved the direct surgical care of patients were included. The survey was completed electronically and the results were evaluated using chi-squared analysis to determine which independent variables contributed to a dependent outcome of changing place of employment. RESULTS: 110 surveys were returned with respondents meeting inclusion criteria. 13 (11.8%) of the respondents changed jobs within the study period and 97 (88.2%) did not change jobs. Factors identified that likely contributed to changing jobs included a perceived lack of opportunity for career [p = <0.001] advancement and the desire to no longer work at an academic or teaching facility [p = 0.013]. Others factors included excessive case load [p = 0.006]; personal conflict with partners or staff [p = 0.007]; career goals unfulfilled by practice [p = 0.011]; lack of mentorship in partners [p = 0.026]; and desire to be closer to the surgeon's or their spouse's family [p = 0.002]. CONCLUSIONS: Several factors appear to play a role in motivating young pediatric surgeons to change jobs early in their careers. These factors should be taken into account by senior pediatric fellows and their advisors when considering job opportunities

Pan-cancer image-based detection of clinically actionable genetic alterations

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their microenvironment. Routine histopathology tissue slides, which are ubiquitously available, can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5,000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype–phenotype links in cancer

Deep learning generates synthetic cancer histology for explainability and education

Abstract Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology

Author Correction:Pan-cancer image-based detection of clinically actionable genetic alterations (Nature Cancer, (2020), 1, 8, (789-799), 10.1038/s43018-020-0087-6)

In the version of this article initially published, the sample size (n = 794) was incorrect in Fig. 2f and Extended Data Fig. 4a,e; the correct sample size is ‘n = 397’. The sample size (n = 826) was also incorrect in Fig. 2h and Extended Data Fig. 4q,u; the correct sample size is ‘n = 413’. Also, the values in Supplementary Table 2, row ‘TCGA-HNSC’, column ‘Quality OK and tumor on slide’ (424, 424) were incorrect;the correct values are ‘457, 439’. The errors have been corrected in the HTML and PDF versions of the article