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Optical coherence tomography angiography (OCTA) covers most functions of fluorescein angiography (FA) when imaging the retina but lacks the ability to depict vascular leakage. Based on OCTA, we developed artificial intelligence-inferred-FA (AI-FA) to delineate leakage in eyes with diabetic retinopathy (DR). Training data of 19,648 still FA images were prepared from FA-photo and videos of 43 DR eyes. AI-FA images were generated using a convolutional neural network. AI-FA images achieved a structural similarity index of 0.91 with corresponding real FA images in DR. The AI-FA generated from OCTA correctly depicted vascular occlusion and associated leakage with enough quality, enabling precise DR diagnosis and treatment planning. A combination of OCT, OCTA, and AI-FA yields more information than real FA with reduced acquisition time without risk of allergic reactions.

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BACKGROUND AND AIM: To better predict patient survival, we used automated tumor volume and density measurements to make an objective radiological assessment of the response of advanced hepatocellular carcinoma (HCC) to treatment with sorafenib. METHODS: Patients treated with sorafenib were identified retrospectively. Those who were diagnosed with Child-Pugh class A liver function, Barcelona-Clinic Liver Cancer stage C, and Eastern Cooperative Oncology Group performance status grade 0/1 were enrolled (n = 22). Reviews of contrast-enhanced computed tomography images were supported by the automated measurement of lesions using computer software. Treatment responses were assessed using volume and density criteria. Kaplan-Meier methods and multivariate Cox regression analysis were used to evaluate treatment responses and identify the most significant prognostic factors for overall survival (OS). RESULTS: After patients were dichotomized according to volume and density criteria, the median OS for those with an objective response (OR) (complete response + partial response) was 20.4 months and that for those with a non-OR (stable disease + progressive disease) was 9.3 months (P = 0.009). The best multivariate regression model for survival identified volume and density criteria (OR or non-OR) as a significant variable, along with baseline alpha-fetoprotein levels (log-rank test, P = 0.01). No other conventional criteria were identified as significant. CONCLUSIONS: Tumor volume and density assessment using automated lesion measurements may be an objective method of evaluating responses of advanced HCC to treatment with sorafenib.

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To evaluate the improvement of radiologist performance in detecting bone metastases at follow up low-dose computed tomography (CT) by using a temporal subtraction (TS) technique based on an advanced nonrigid image registration algorithm.Twelve patients with bone metastases (males, 5; females, 7; mean age, 64.8 ±â€Š7.6 years; range 51-81 years) and 12 control patients without bone metastases (males, 5; females, 7; mean age, 64.8 ±â€Š7.6 years; 51-81 years) were included, who underwent initial and follow-up CT examinations between December 2005 and July 2016. Initial CT images were registered to follow-up CT images by the algorithm, and TS images were created. Three radiologists independently assessed the bone metastases with and without the TS images. The reader averaged jackknife alternative free-response receiver operating characteristics figure of merit was used to compare the diagnostic accuracy.The reader-averaged values of the jackknife alternative free-response receiver operating characteristics figures of merit (θ) significantly improved from 0.687 for the readout without TS and 0.803 for the readout with TS (P value = .031. F statistic = 5.24). The changes in the absolute value of CT attenuations in true-positive lesions were significantly larger than those in false-negative lesions (P < .001). Using TS, segment-based sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the readout with TS were 66.7%, 98.9%, 94.4%, 90.9%, and 94.8%, respectively.The TS images can significantly improve the radiologist's performance in the detection of bone metastases on low-dose and relatively thick-slice CT.

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Multiple myeloma (MM) is a clonal plasma cell disorder originating in bone marrow. Whole body low-dose multidetector CT (MDCT) can depict bone marrow infiltration by myeloma cells into the adipose-rich fatty marrow of the appendicular skeleton. However, automated and objective volume measurement of bone marrow infiltration has not been established, and its clinical relevance remains unclear. We therefore developed novel CT post-processing software (MABLE software) and measured the total sum of CT values (cumulative CT value, cCTv) representing bone marrow infiltration, by combining volume and voxel-based CT values. The cCTv was greater in patients with symptomatic MM than in those with smouldering MM or monoclonal gammopathy of unknown significance. Patients with revised International Staging System (R-ISS) III had a higher cCTv than those with R-ISS I or II. Age, albumin, and M-protein levels independently predicted cCTv. Mixed graphical model analysis revealed direct relationships between cCTv and age or R-ISS. Tree-structured survival analysis and multivariate Cox analysis revealed that a cCTv greater than or equal to 4.4 was independently prognostic for overall survival. Anti-myeloma therapy reduced cCTv after treatment. These findings suggest that the automatically calculated cCTv reflects disease aggressiveness and is useful for accurate prognostic prediction in MM patients.

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BACKGROUND: Although tumor response evaluated with radiological imaging is frequently used as a primary endpoint in clinical trials, it is difficult to obtain precise results because of inter- and intra-observer differences. PURPOSE: To evaluate usefulness of a cloud-based local-read paradigm implementing software solutions that standardize imaging evaluations among international investigator sites for clinical trials of lung cancer. MATERIAL AND METHODS: Two studies were performed: KUMO I and KUMO I Extension. KUMO I was a pilot study aiming at demonstrating the feasibility of cloud implementation and identifying issues regarding variability of evaluations among sites. Chest CT scans at three time-points from baseline to progression, from 10 patients with lung cancer who were treated with EGFR tyrosine kinase inhibitors, were evaluated independently by two oncologists (Japan) and one radiologist (France), through a cloud-based software solution. The KUMO I Extension was performed based on the results of KUMO I. RESULTS: KUMO I showed discordance rates of 40% for target lesion selection, 70% for overall response at the first time-point, and 60% for overall response at the second time-point. Since the main reason for the discordance was differences in the selection of target lesions, KUMO I Extension added a cloud-based quality control service to achieve a consensus on the selection of target lesions, resulting in an improved rate of agreement of response evaluations. CONCLUSION: The study shows the feasibility of imaging evaluations at investigator sites, based on cloud services for clinical studies involving multiple international sites. This system offers a step forward in standardizing evaluations of images among widely dispersed sites.

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RATIONALE AND OBJECTIVES: Lesion volume is considered as a promising alternative to Response Evaluation Criteria in Solid Tumors (RECIST) to make tumor measurements more accurate and consistent, which would enable an earlier detection of temporal changes. In this article, we report the results of a pilot study aiming at evaluating the effects of a consensual lesion selection on volume-based response (VBR) assessments. MATERIALS AND METHODS: Eleven patients with lung computed tomography scans acquired at three time points were selected from Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) and proprietary databases. Images were analyzed according to RECIST 1.1 and VBR criteria by three readers working in different geographic locations. Cloud solutions were used to connect readers and carry out a consensus process on the selection of lesions used for computing response. Because there are not currently accepted thresholds for computing VBR, we have applied a set of thresholds based on measurement variability (-35% and +55%). The benefit of this consensus was measured in terms of multiobserver agreement by using Fleiss kappa (κfleiss) and corresponding standard errors (SE). RESULTS: VBR after consensual selection of target lesions allowed to obtain κfleiss = 0.85 (SE = 0.091), which increases up to 0.95 (SE = 0.092), if an extra consensus on new lesions is added. As a reference, the agreement when applying RECIST without consensus was κfleiss = 0.72 (SE = 0.088). These differences were found to be statistically significant according to a z-test. CONCLUSIONS: An agreement on the selection of lesions allows reducing the inter-reader variability when computing VBR. Cloud solutions showed to be an interesting and feasible strategy for standardizing response evaluations, reducing variability, and increasing consistency of results in multicenter clinical trials.

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