Purpose: Inotuzumab ozogamicin (INO) has demonstrated a safe bridging role to allogeneic hematopoietic stem cell transplantation (HSCT) in patients with relapsed or refractory B-cell acute lymphoblastic leukemia (R/R B-ALL). How‑ ever, hepatic veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is frequently observed. This study aimed to identify significant features of INO-associated VOD/SOS. Methods: We reviewed seven cases of hepatic VOD/SOS that developed either during INO salvage or after alloge‑ neic HSCT following INO-induced complete remission (CR). Diagnosis and severity grading of VOD/SOS were based on the revised criteria from the European Society for Blood and Marrow Transplantation. Defibrotide was used to treat severe to very severe cases. Results: Four patients developed VOD/SOS during INO salvage therapy (at 21 and 36 days post-INO1, 77 days postINO3, and 21 days post-INO5), while three were diagnosed at 2, 5, and 10 days post-HSCT following INO-induced CR.Doppler ultrasonography revealed preserved portal vein flow (range 10.2–26.0 cm/sec) and normal hepatic artery resistive index (RI, range 0.56–0.74) in all but one patient (RI 0.83). Despite this, all patients presented with massive ascites and progressively elevated total bilirubin levels. All cases were classified as severe to very severe; six were treated with defibrotide and one underwent liver transplantation. Most patients ultimately died owing to VOD/SOS progression. Conclusion Post-INO VOD/SOS manifested as two different clinical settings and was characterized by preserved portal vein flow, which complicated diagnosis. Despite timely defibrotide administration, clinical outcomes were poor.These findings emphasize the need for vigilance and potential consideration of prophylactic strategies for prevention of INO-associated VOD/SOS.

Purpose: Inotuzumab ozogamicin (INO) has demonstrated a safe bridging role to allogeneic hematopoietic stem cell transplantation (HSCT) in patients with relapsed or refractory B-cell acute lymphoblastic leukemia (R/R B-ALL). How‑ ever, hepatic veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is frequently observed. This study aimed to identify significant features of INO-associated VOD/SOS. Methods: We reviewed seven cases of hepatic VOD/SOS that developed either during INO salvage or after alloge‑ neic HSCT following INO-induced complete remission (CR). Diagnosis and severity grading of VOD/SOS were based on the revised criteria from the European Society for Blood and Marrow Transplantation. Defibrotide was used to treat severe to very severe cases. Results: Four patients developed VOD/SOS during INO salvage therapy (at 21 and 36 days post-INO1, 77 days postINO3, and 21 days post-INO5), while three were diagnosed at 2, 5, and 10 days post-HSCT following INO-induced CR.Doppler ultrasonography revealed preserved portal vein flow (range 10.2–26.0 cm/sec) and normal hepatic artery resistive index (RI, range 0.56–0.74) in all but one patient (RI 0.83). Despite this, all patients presented with massive ascites and progressively elevated total bilirubin levels. All cases were classified as severe to very severe; six were treated with defibrotide and one underwent liver transplantation. Most patients ultimately died owing to VOD/SOS progression. Conclusion Post-INO VOD/SOS manifested as two different clinical settings and was characterized by preserved portal vein flow, which complicated diagnosis. Despite timely defibrotide administration, clinical outcomes were poor.These findings emphasize the need for vigilance and potential consideration of prophylactic strategies for prevention of INO-associated VOD/SOS.

Purpose: Inotuzumab ozogamicin (INO) has demonstrated a safe bridging role to allogeneic hematopoietic stem cell transplantation (HSCT) in patients with relapsed or refractory B-cell acute lymphoblastic leukemia (R/R B-ALL). How‑ ever, hepatic veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is frequently observed. This study aimed to identify significant features of INO-associated VOD/SOS. Methods: We reviewed seven cases of hepatic VOD/SOS that developed either during INO salvage or after alloge‑ neic HSCT following INO-induced complete remission (CR). Diagnosis and severity grading of VOD/SOS were based on the revised criteria from the European Society for Blood and Marrow Transplantation. Defibrotide was used to treat severe to very severe cases. Results: Four patients developed VOD/SOS during INO salvage therapy (at 21 and 36 days post-INO1, 77 days postINO3, and 21 days post-INO5), while three were diagnosed at 2, 5, and 10 days post-HSCT following INO-induced CR.Doppler ultrasonography revealed preserved portal vein flow (range 10.2–26.0 cm/sec) and normal hepatic artery resistive index (RI, range 0.56–0.74) in all but one patient (RI 0.83). Despite this, all patients presented with massive ascites and progressively elevated total bilirubin levels. All cases were classified as severe to very severe; six were treated with defibrotide and one underwent liver transplantation. Most patients ultimately died owing to VOD/SOS progression. Conclusion Post-INO VOD/SOS manifested as two different clinical settings and was characterized by preserved portal vein flow, which complicated diagnosis. Despite timely defibrotide administration, clinical outcomes were poor.These findings emphasize the need for vigilance and potential consideration of prophylactic strategies for prevention of INO-associated VOD/SOS.

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters;minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians’ decisionmaking by real-time application in the catheterization laboratory.

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs.We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

Background: Arsenic trioxide (ATO) is the standard treatment for relapsed acute promyelocytic leukemia (APL). However, consensus on post-remission therapies is still lacking. Methods: We evaluated 52 patients who experienced relapse following initial treatment of APL between 2000 and 2019 at Catholic Hematology Hospital. Among them, 41 patients received reinduction treatment, 30 with ATO-based regimen, whereas 11 with conventional intensive chemotherapy (IC). Results: The ATO reinduction group showed a significantly higher second molecular complete remission (mCR2) rate, superior neutrophil and platelet recovery, and a lower infection rate than the IC reinduction group. No significant differences were observed in survival outcomes after post-remission treatment among the ATO-based (N=19), autologous (N=12), and allogeneic (N=6) hematopoietic stem cell transplantation (HSCT) groups. In the ATO-based and autologous HSCT groups, among patients with mCR2 after ATO reinduction, nine and five patients experienced a second relapse, respectively (50.7% vs. 41.7%, P =0.878). Among these patients, seven received salvage allogeneic HSCT; six remained alive. The other seven patients received ATO without HSCT. Five died from disease progression, and two survived and have been in mCR2 since. Conclusion Post-remission treatment outcomes of patients with relapsed APL were not significantly different, regardless of the treatment option, suggesting the feasibility of ATO-based treatment without HSCT in mCR2. Allogeneic HSCT may be an effective salvage treatment modality for patients with a second relapse. Owing to a few cases of relapsed APL, multicenter prospective studies may help elucidate the efficacy of each post-remission treatment.

Background/Aims: Idiopathic multicentric Castleman disease (iMCD) comprises approximately 30% of all cases of Castleman disease. It is characterized by constitutional symptoms, enlarged lymph nodes at multiple anatomical sites, and laboratory test abnormalities, which are primarily related to the overproduction of interleukin 6 (IL-6). Siltuximab is a human-mouse chimeric immunoglobulin G1κ monoclonal antibody against human IL-6. In view of the limited treatment options for iMCD, this study aimed to evaluate the efficacy and safety of siltuximab in the management of this condition. Methods: In this real-world retrospective study, we administered siltuximab to 15 patients with iMCD who previously received conventional chemotherapy and/or steroid pulse therapy. The median time to a durable symptomatic response was 22 days (range, 17 to 56). The serum hemoglobin and albumin levels and erythrocyte sedimentation rates significantly normalized after the first 3 months of siltuximab treatment. Lymph node involution, assessed using imaging, was relatively gradual, demonstrating a complete or partial response at 6 months. Results: On an average, the improvements in clinical, laboratory, and radiologic parameters of iMCD in responders were observed after one, three, and eight cycles of siltuximab treatment, respectively. Siltuximab demonstrated a favorable safety profile, and prolonged treatment was well-tolerated. Conclusions Despite the small sample size of the present study, the results are encouraging and demonstrate the potential of siltuximab as the first-line treatment of iMCD. Further large multicenter studies are needed to evaluate the clinical outcomes and adverse events associated with siltuximab.

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1–10, 11–100, 101–400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and falsepositive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1–16 segments) were high (85.5– 100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images with different slice thicknesses. MATERIALS AND METHODS: CT images with 1-, 3-, and 5-mm slice thicknesses obtained from 100 pathologically proven lung cancers between July 2017 and December 2017 were evaluated. CNN-based SR algorithms using residual learning were developed to convert thick-slice images into 1-mm slices. Lung cancers were semi-automatically segmented and a total of 702 RFs (tumor intensity, texture, and wavelet features) were extracted from 1-, 3-, and 5-mm slices, as well as the 1-mm slices generated from the 3- and 5-mm images. The stabilities of the RFs were evaluated using concordance correlation coefficients (CCCs). RESULTS: The mean CCCs for the comparisons of original 1 mm vs. 3 mm, 1 mm vs. 5 mm, and 3 mm vs. 5 mm images were 0.41, 0.27, and 0.65, respectively (p < 0.001 for all comparisons). Tumor intensity features showed the best reproducibility while wavelets showed the lowest reproducibility. The majority of RFs failed to achieve reproducibility (CCC ≥ 0.85; 3.6%, 1.0%, and 21.5%, respectively). After applying the CNN-based SR algorithms, the reproducibility significantly improved in all three pairings (mean CCCs: 0.58, 0.45, and 0.72; p < 0.001 for all comparisons). The reproducible RFs also increased (36.3%, 17.4%, and 36.9%, respectively). CONCLUSION: The reproducibility of RFs in lung cancer is significantly influenced by CT slice thickness, which can be improved by the CNN-based SR algorithms.
Learning ; Lung Neoplasms ; Lung ; Retrospective Studies