Evaluation of the entire vagina by colposcopy is warranted in each patient with abnormal cervical screening results. The predominant HPV genotypes among patients with VAIN could be used to establish diagnosis program and develop an HPV vaccine.

Objective: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. Background: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. Methods: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker—DeepCT-PDAC—by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. Results: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50–2.75; HR: 2.47, CI: 1.35–4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89–3.28; HR: 2.15, CI: 1.14–4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19–0.64), but did not affect OS in the subgroup with high risk. Conclusions: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

No abstract

No abstract

The metastatic or recurrent potential of localized human papillomavirus-associated endocervical adenocarcinoma (HPVA EAC) is difficult to predict, especially based upon biopsy alone. Recent analyses of small cohorts indicate that high tumor nuclear grade (TNG) and the presence of necrotic tumor debris (NTD) from HPVA EACs in cervical biopsy specimens are highly predictive of nodal metastasis (NM). In the present study, we aimed to investigate how reliably tumoral morphologic features from cervical biopsy specimens predict NM or tumor recurrence (TR) and patient outcomes in a large cohort of endocervical adenocarcinoma patients. A cohort comprised of 397 patients with HPVA EAC treated at 18 institutions was identified, and cervical biopsies were paired with their associated complete tumor resections for a total of 794 specimens. A variety of tumoral histologic features were examined for each paired specimen, including TNG (assessed on a 3-tiered scale of increasing abnormalities-TNG1, TNG2, TNG3) and NTD (defined by the presence of necrotic and apoptotic tumor cells within tumor glandular lumens admixed with granular and eosinophilic amorphous material and inflammatory cells), which were correlated with outcomes. The distribution of TNG in biopsies was as From the