Med Phys
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Intraductal papillary mucinous neoplasms (IPMNs) are radiographically visible precursor lesions of pancreatic cancer. Despite standard criteria for assessing risk, only 18% of cysts are malignant at resection. Thus, a large number of patients undergo unnecessary invasive surgery for benign disease. The ability to identify IPMNs with low or high risk of transforming into invasive cancer would optimize patient selection and improve surgical decision-making. The purpose of this study was to investigate quantitative CT imaging features as markers for objective assessment of IPMN risk. ⋯ The present study demonstrates that features extracted from pretreatment CT images can predict the risk of IPMN. Development of a preoperative model to discriminate between low-risk and high-risk IPMN will improve surgical decision-making.
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With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. ⋯ The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.
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Intensity modulated radiation therapy (IMRT) is commonly employed for treating head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing. Accurate delineation of organs-at-risk (OARs) on H&N CT images is thus essential to treatment quality. Manual contouring used in current clinical practice is tedious, time-consuming, and can produce inconsistent results. Existing automated segmentation methods are challenged by the substantial inter-patient anatomical variation and low CT soft tissue contrast. To overcome the challenges, we developed a novel automated H&N OARs segmentation method that combines a fully convolutional neural network (FCNN) with a shape representation model (SRM). ⋯ Experiments on clinical datasets of H&N patients demonstrated the effectiveness of the proposed deep neural network segmentation method for multi-organ segmentation on volumetric CT scans. The accuracy and robustness of the segmentation were further increased by incorporating shape priors using SMR. The proposed method showed competitive performance and took shorter time to segment multiple organs in comparison to state of the art methods.
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To correlate the accumulated thermal dose (ATD) with lesion size in magnetic resonance (MR)-guided focused ultrasound (MRgFUS) thalamotomy to help guide future clinical treatments. ⋯ The ATD was correlated with lesion size measured 1 day following MRgFUS thalamotomy for essential tremor. These data provide useful information for predicting brain lesion size and determining treatment endpoints in future clinical MRgFUS procedures.
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Spectral CT using a dual layer detector offers the possibility of retrospectively introducing spectral information to conventional CT images. In theory, the dual-layer technology should not come with a dose or image quality penalty for conventional images. In this study, we evaluate the influence of a dual-layer detector (IQon Spectral CT, Philips Healthcare) on the image quality of conventional CT images, by comparing these images with those of a conventional but otherwise technically comparable single-layer CT scanner (Brilliance iCT, Philips Healthcare), by means of phantom experiments. ⋯ At equivalent dose levels, this study showed similar quality of conventional images acquired on iCT and IQon for medium-sized phantoms and slightly degraded image quality for (very) large phantoms at lower tube voltages on the IQon. Accordingly, it may be concluded that the introduction of a dual-layer detector neither compromises image quality of conventional images nor increases radiation dose for normal-sized patients, and slightly degrades dose efficiency for large patients at 120 kVp and lower tube voltages.