Objective To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Summary of background data Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis. Methods A DCNN was pretrained using 25,505 limb radiographs between January 2012 and December 2017. It was retrained using 3605 PXRs between August 2008 and December 2016. The accuracy, sensitivity, false-negative rate, and area under the receiver operating characteristic curve (AUC) were evaluated on 100 independent PXRs acquired during 2017. The authors also used the visualization algorithm gradient-weighted class activation mapping (Grad-CAM) to confirm the validity of the model. Results The algorithm achieved an accuracy of 91%, a sensitivity of 98%, a false-negative rate of 2%, and an AUC of 0.98 for identifying hip fractures. The visualization algorithm showed an accuracy of 95.9% for lesion identification. Conclusions A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions. The DCNN might be an efficient and economical model to help clinicians make a diagnosis without interrupting the current clinical pathway. Key Points • Automated detection of hip fractures on frontal pelvic radiographs may facilitate emergent screening and evaluation efforts for primary physicians. • Good visualization of the fracture site by Grad-CAM enables the rapid integration of this tool into the current medical system. • The feasibility and efficiency of utilizing a deep neural network have been confirmed for the screening of hip fractures.

Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960–0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948–0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912–0.936), 0.908 (95% CI, 0.885–0.908), and 0.932 (95% CI, 0.919–0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.

Antitumor activity of the combination of an HSP90 inhibitor and a PI3K/mTOR dual inhibitor against cholangiocarcinoma Antitumor activity of the combination of an HSP90 inhibitor and a PI3K/mTOR dual inhibitor against cholangiocarcinoma

Purpose: Intrahepatic cholangiocarcinoma is a fatal primary liver cancer resulting from diagnosis at an advanced stage. Understanding the mechanisms of drug resistance and metastasis of cholangiocarcinoma may improve the disease prognosis. Enhanced aldehyde dehydrogenase (ALDH) activity is suggested to be associated with increased drug resistance and the metastasis. This study aims to investigate the roles of the ALDH isoforms in cholangiocarcinoma. Experimental Design: Aldefluor assays, RT-PCR, and Western blot analysis were used to identify the major ALDH isoforms contributing to Aldefluor activity in human cholangiocarcinoma cell lines. We manipulated isoform expression in HuCCT1 cells to elucidate the role of ALDH1A3 in the malignant progression of these cells. Finally, we used immunohistochemical staining to evaluate the clinical significance of ALDH1A3 in 77 hepatectomized cholangiocarcinoma patients and an additional 31 patients with advanced cholangiocarcinoma who received gemcitabine-based therapy. Results: ALDH^high cholangiocarcinoma cells not only migrated faster but were more resistant to gemcitabine. Among the 19 ALDH isoforms studied, ALDH1A3 was found to be the main contributor to Aldefluor activity. In addition, we also found that knockdown of ALDH1A3 expression in HuCCT1 cells markedly reduced not only their sensitivity to gemcitabine, which might be attributed to a decreased expression of ribonucleotide reductase M1, but also their migration. Most importantly, this enzyme was also identified as an independent poor prognostic factor for patients with intrahepatic cholangiocarcinoma, as well as a prognostic biomarker of gemcitabine-treated patients. Conclusions: ALDH1A3 plays an important role in enhancing malignant behavior of cholangiocarcinoma and serves as a new therapeutic target. Clin Cancer Res; 22(16); 4225–35. ©2016 AACR.