No abstract

No abstract

No abstract

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features; and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low-and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches.

BACKGROUND: Given the costs of delivering care for men with prostate cancer remain poorly described, this article reports the results of time-driven activity-based costing (TDABC) for competing treatments of low-risk prostate cancer. METHODS: Process maps were developed for each phase of care from the initial urologic visit through 12 years of follow-up for robotic-assisted laparoscopic prostatectomy (RALP), cryotherapy, high-dose rate (HDR) and low-dose rate (LDR) brachytherapy, intensity-modulated radiation therapy (IMRT), stereotactic body radiation therapy (SBRT), and active surveillance (AS). The last modality incorporated both traditional transrectal ultrasound (TRUS) biopsy and multiparametric-MRI/TRUS fusion biopsy. The costs of materials, equipment, personnel, and space were calculated per unit of time and based on the relative proportion of capacity used. TDABC for each treatment was defined as the sum of its resources. RESULTS: Substantial cost variation was observed at 5 years, with costs ranging from $7,298 for AS to $23,565 for IMRT, and they remained consistent through 12 years of follow-up. LDR brachytherapy ($8,978) was notably cheaper than HDR brachytherapy ($11,448), and SBRT ($11,665) was notably cheaper than IMRT, with the cost savings attributable to shorter procedure times and fewer visits required for treatment. Both equipment costs and an inpatient stay ($2,306) contributed to the high cost of RALP ($16,946). Cryotherapy ($11,215) was more costly than LDR brachytherapy, largely because of increased singleuse equipment costs ($6,292 vs $1,921). AS reached cost equivalence with LDR brachytherapy after 7 years of follow-up. CONCLU-SIONS: The use of TDABC is feasible for analyzing cancer services and provides insights into cost-reduction tactics in an era focused on emphasizing value. By detailing all steps from diagnosis and treatment through 12 years of follow-up for low-risk prostate cancer, this study has demonstrated significant cost variation between competing treatments. Cancer 2016;122:447-55. V C 2015 American Cancer Society.