ABSTRACT
Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics with the highest mortality among gynecological malignancies. Accurate and early diagnosis of ovarian cancer is of great significance. Here, we present OvcaFinder, an interpretable model constructed from ultrasound images-based deep learning (DL) predictions, Ovarian-Adnexal Reporting and Data System scores from radiologists, and routine clinical variables. OvcaFinder outperforms the clinical model and the DL model with area under the curves (AUCs) of 0.978, and 0.947 in the internal and external test datasets, respectively. OvcaFinder assistance led to improved AUCs of radiologists and inter-reader agreement. The average AUCs were improved from 0.927 to 0.977 and from 0.904 to 0.941, and the false positive rates were decreased by 13.4% and 8.3% in the internal and external test datasets, respectively. This highlights the potential of OvcaFinder to improve the diagnostic accuracy, and consistency of radiologists in identifying ovarian cancer.
Subject(s)
Ovarian Neoplasms , Female , Humans , Ovarian Neoplasms/diagnostic imaging , Area Under Curve , Extremities , Radiologists , Retrospective StudiesABSTRACT
@#The standardized workflow of computer-aided static guided implant surgery includes preoperative examination, data acquisition, guide design, guide fabrication and surgery. Errors may occur at each step, leading to irreversible cumulative effects and thus impacting the accuracy of implant placement. However, clinicians tend to focus on factors causing errors in surgical operations, ignoring the possibility of irreversible errors in nonstandard guided surgery. Based on the clinical practice of domestic experts and research progress at home and abroad, this paper summarizes the sources of errors in guided implant surgery from the perspectives of preoperative inspection, data collection, guide designing and manufacturing and describes strategies to resolve errors so as to gain expert consensus. Consensus recommendation: 1. Preoperative considerations: the appropriate implant guide type should be selected according to the patient's oral condition before surgery, and a retaining screw-assisted support guide should be selected if necessary. 2. Data acquisition should be standardized as much as possible, including beam CT and extraoral scanning. CBCT performed with the patient’s head fixed and with a small field of view is recommended. For patients with metal prostheses inside the mouth, a registration marker guide should be used, and the ambient temperature and light of the external oral scanner should be reasonably controlled. 3. Optimization of computer-aided design: it is recommended to select a handle-guided planting system and a closed metal sleeve and to register images by overlapping markers. Properly designing the retaining screws, extending the support structure of the guide plate and increasing the length of the guide section are methods to feasibly reduce the incidence of surgical errors. 4. Improving computer-aided production: it is also crucial to set the best printing parameters according to different printing technologies and to choose the most appropriate postprocessing procedures.
ABSTRACT
Background Use of contrast material-enhanced (CE) US Liver Imaging Reporting and Data System (LI-RADS) version 2017 has not been validated in large populations where hepatitis B virus (HBV) is endemic. Purpose To evaluate the diagnostic performance of CE US LI-RADS version 2017 in a population with a high prevalence of HBV infection. Materials and Methods In this retrospective study, liver nodules in patients with HBV who were evaluated from January 2004 to December 2016 were categorized as CE US LR-1 to LR-5 through LR-M. A subgroup of LR-M nodules was reclassified as LR-5, and additional analysis was performed. The reference standard consisted of histologic evaluation or composite imaging and clinical follow-up findings. Diagnostic performance was assessed with sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Results A total of 2020 nodules in 1826 patients (median age, 54 years ± 12 [standard deviation]; 1642 men) were included. Of the 1159 LR-5 lesions, 1141 were hepatocellular carcinoma (HCC); three, intrahepatic cholangiocarcinomas; six, other malignancies; six, atypical hyperplasia; and three, benign lesions. The PPV of LR-5 for HCC was 98% (95% confidence interval [CI]: 98%, 99%). In LR-M nodules, 153 showed arterial phase hyperenhancement, early washout, and absence of punched-out appearance within 5 minutes, and 142 of 153 (93%; 95% CI: 89%, 97%) were HCC. If these nodules were reclassified as LR-5, LR-M specificity and PPV as a predictor of non-HCC malignancy increased from 88% (95% CI: 87%, 89%) and 36% (95% CI: 31%, 41%) to 96% (95% CI: 95%, 97%) and 58% (95% CI: 51%, 65%), respectively (P < .001). Despite reclassification, LR-5 specificity and PPV remained high (94% [95% CI: 92%, 96%] and 98% [95% CI: 97%, 99%], respectively). Conclusion The contrast-enhanced US Liver Imaging Reporting and Data System version 2017 category LR-5 is effectively predictive of the presence of hepatocellular carcinoma. In patients with hepatitis B virus infection, performance may be further improved by reclassification of category LR-M nodules with arterial phase hyperenhancement, early washout, and no punched-out appearance to LR-5. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Sidhu in this issue.
