Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images.

OBJECTIVES: To apply image registration in the follow up of lung nodules and verify the feasibility of automatic tracking of lung nodules using an artificial intelligence (AI) method. METHODS: For this retrospective, observational study, patients with pulmonary nodules 5-30 mm in diameter on computed tomography (CT) and who had at least six months follow-up were identified. Two radiologists defined a 'correct' cuboid circumscribing each nodule which was used to judge the success/failure of nodule tracking. An AI algorithm was applied in which a U-net type neural network model was trained to predict the deformation vector field between two examinations. When the estimated position was within a defined cuboid, the AI algorithm was judged a success. RESULTS: In total, 49 lung nodules in 40 patients, with a total of 368 follow-up CT examinations were examined. The success rate for each time evaluation was 94% (345/368) and for 'nodule-by-nodule evaluation' was 78% (38/49). Reasons for a decrease in success rate were related to small nodules and those that decreased in size. CONCLUSION: Automatic tracking of lung nodules is highly feasible.

Radiological features of thyroid-like low-grade nasopharyngeal papillary adenocarcinoma: case series and systematic review.

PURPOSE: To comprehensively summarize the clinical data and CT/MRI characteristics of thyroid-like low-grade nasopharyngeal papillary adenocarcinoma (TL-LGNPPA). METHODS: Twenty-seven lesions from 25 study articles identified through a systematic review and three lesions from our institution associated with TL-LGNPPA were evaluated. RESULTS: The mean age of the patients at diagnosis was 35.7 years, and the male-to-female ratio was nearly half. The chief complaint was nasal obstruction, followed by epistaxis. All patients underwent excision. None of the patients had neck nodes or distant metastases. All patients survived with no locoregional/distant recurrence during 3-93 months of follow-up. All lesions were located at the posterior edge of the nasal septum, attached to the nasopharyngeal parietal wall, and showed no laterality. The mean lesion diameter was 1.7 cm. The margins of lesions were well-defined and lobulated, followed by well-defined smooth margins. None of lesions were associated with parapharyngeal space or skull base destruction. All lesions were iso- and low-density on non-contrast CT. Adjacent skull base sclerosis was detected in 63.6% of lesions. High signal intensity on T2-weighted imaging and mostly iso-signal intensity on T1-weighted imaging compared to muscle tissue. Most lesions were heterogeneous and exhibited moderate contrast enhancement. Relatively large lesions (≥1.4 cm) tended to be more lobulated than smooth margins compared to relatively small lesions (<1.4 cm) (p = 0.016). CONCLUSION: We summarized the clinical and radiological features of TL-LGNPPA to facilitate accurate diagnosis and appropriate management.

Development and performance evaluation of a deep learning lung nodule detection system.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small nodule shadow from 3D volume images, which is very burdensome and often results in missed nodules. To address these challenges, we developed a computer-aided detection (CAD) system that automatically detects lung nodules in CT images. METHODS: A total of 1997 chest CT scans were collected for algorithm development. The algorithm was designed using deep learning technology. In addition to evaluating detection performance on various public datasets, its robustness to changes in radiation dose was assessed by a phantom study. To investigate the clinical usefulness of the CAD system, a reader study was conducted with 10 doctors, including inexperienced and expert readers. This study investigated whether the use of the CAD as a second reader could prevent nodular lesions in lungs that require follow-up examinations from being overlooked. Analysis was performed using the Jackknife Free-Response Receiver-Operating Characteristic (JAFROC). RESULTS: The CAD system achieved sensitivity of 0.98/0.96 at 3.1/7.25 false positives per case on two public datasets. Sensitivity did not change within the range of practical doses for a study using a phantom. A second reader study showed that the use of this system significantly improved the detection ability of nodules that could be picked up clinically (p = 0.026). CONCLUSIONS: We developed a deep learning-based CAD system that is robust to imaging conditions. Using this system as a second reader increased detection performance.

Clinical validity of non-contrast-enhanced VI-RADS: prospective study using 3-T MRI with high-gradient magnetic field.

OBJECTIVES: To develop a modified Vesical Imaging Reporting and Data System (VI-RADS) without dynamic contrast-enhanced imaging (DCEI), termed "non-contrast-enhanced VI-RADS (NCE-VI-RADS)", and to assess the additive impact of denoising deep learning reconstruction (dDLR) on NCE-VI-RADS. METHODS: From January 2019 through December 2020, 163 participants who underwent high-gradient 3-T MRI of the bladder were prospectively enrolled. In total, 108 participants with pathologically confirmed bladder cancer by transurethral resection were analyzed. Tumors were evaluated based on VI-RADS (scores 1-5) by two readers independently: an experienced radiologist (reader 1) and a senior radiology resident (reader 2). Conventional VI-RADS assessment included all three imaging types (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI], and dynamic contrast-enhanced imaging [DCEI]). Also evaluated were NCE-VI-RADS comprising only non-contrast-enhanced imaging types (T2WI and DWI), and "NCE-VI-RADS with dDLR" comprising T2WI processed with dDLR and DWI. All systems were assessed using receiver-operating characteristic curve analysis and simple and/or weighted κ statistics. RESULTS: Muscle invasion was identified in 23/108 participants (21%). Area under the curve (AUC) values for diagnosing muscle invasion were as follows: conventional VI-RADS, 0.94 and 0.91; NCE-VI-RADS, 0.93 and 0.91; and "NCE-VI-RADS with dDLR", 0.96 and 0.93, for readers 1 and 2, respectively. Simple κ statistics indicated substantial agreement for NCE-VI-RADS and almost perfect agreement for conventional VI-RADS and "NCE-VI-RADS with dDLR" between the two readers. CONCLUSION: NCE-VI-RADS achieved predictive accuracy for muscle invasion comparable to that of conventional VI-RADS. Additional use of dDLR improved the diagnostic accuracy of NCE-VI-RADS. KEY POINTS: • Non-contrast-enhanced Vesical Imaging Reporting and Data System (NCE-VI-RADS) was developed to avoid risk related to gadolinium-based contrast agent administration. • NCE-VI-RADS had predictive accuracy for muscle invasion comparable to that of conventional VI-RADS. • The additional use of denoising deep learning reconstruction (dDLR) might further improve the diagnostic accuracy of NCE-VI-RADS.