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9. Learning from Mistakes: Impact of Careless Responses On Counseling Research Using Amazon's Mechanical Turk

Author

Kim, Nayoung and Oh, JungSu

Abstract

We investigated the effect of careless or insufficient effort (C/IE) responses in a study using Amazon's Mechanical Turk. A factor mixture model was used to identify latent classes based on the pattern of responses with biases and examine the effect of C/IE responses on the fit of the theoretical model.

Published
2023
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23. Deep learning-based measurement of split glomerular filtration rate with 99mTc-diethylenetriamine pentaacetic acid renal scan.

Author

Ha, Sejin, Park, Byung Soo, Han, Sangwon, Oh, Jungsu S., Chae, Sun Young, Kim, Jae Seung, and Moon, Dae Hyuk

Subjects
GLOMERULAR filtration rate, DEEP learning, CONVOLUTIONAL neural networks, PICTURE archiving & communication systems, CREATININE, BLAND-Altman plot
Abstract

Purpose: To develop a deep learning (DL) model for generating automated regions of interest (ROIs) on 99mTc-diethylenetriamine pentaacetic acid (DTPA) renal scans for glomerular filtration rate (GFR) measurement. Methods: Manually-drawn ROIs retrieved from a Picture Archiving and Communications System were used as ground-truth (GT) labels. A two-dimensional U-Net convolutional neural network architecture with multichannel input was trained to generate DL ROIs. The agreement between GFR values from GT and DL ROIs was evaluated using Lin's concordance correlation coefficient (CCC) and slope coefficients for linear regression analyses. Bias and 95% limits of agreement (LOA) were assessed using Bland-Altman plots. Results: A total of 24,364 scans (12,822 patients) were included. Excellent concordance between GT and DL GFR was found for left (CCC 0.982, 95% confidence interval [CI] 0.981–0.982; slope 1.004, 95% CI 1.003–1.004), right (CCC 0.969, 95% CI 0.968–0.969; slope 0.954, 95% CI 0.953–0.955) and both kidneys (CCC 0.978, 95% CI 0.978–0.979; slope 0.979, 95% CI 0.978–0.979). Bland-Altman analysis revealed minimal bias between GT and DL GFR, with mean differences of − 0.2 (95% LOA − 4.4–4.0), 1.4 (95% LOA − 3.5–6.3) and 1.2 (95% LOA − 6.5–8.8) mL/min/1.73 m² for left, right and both kidneys, respectively. Notably, 19,960 scans (81.9%) showed an absolute difference in GFR of less than 5 mL/min/1.73 m². Conclusion: Our DL model exhibited excellent performance in the generation of ROIs on 99mTc-DTPA renal scans. This automated approach could potentially reduce manual effort and enhance the precision of GFR measurement in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer.

Author

Sung, Changhwan, Oh, Jungsu S., Park, Byung Soo, Kim, Su Ssan, Song, Si Yeol, and Lee, Jong Jin

Abstract

Objective: We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance. Methods: We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on 18F-fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after 18F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets. Results: For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98–0.99, 95–98%, and 87–95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM). Conclusion: The 2D slice-based CNN model using 18F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Prediction of [177Lu]Lu-DOTA-TATE therapy response using the absorbed dose estimated from [177Lu]Lu-DOTA-TATE SPECT/CT in patients with metastatic neuroendocrine tumour.

Author

Ha, Sejin, Kim, Yong-il, Oh, Jungsu S., Yoo, Changhoon, Ryoo, Baek-Yeol, and Ryu, Jin-Sook

Subjects
NEUROENDOCRINE tumors, ABSORBED dose, SINGLE-photon emission computed tomography, COMPUTED tomography, PROGRESSION-free survival, TREATMENT effectiveness, THERMOLUMINESCENCE dosimetry, DOSE-response relationship (Radiation)
Abstract

Background: Peptide receptor radionuclide therapy (PRRT) with [177Lu]Lu-DOTA-TATE has shown efficacy in patients with metastatic neuroendocrine tumours (NETs). Personalised dosimetry is crucial to optimise treatment outcomes and minimise adverse events. In this study, we investigated the correlation between the tumour-absorbed dose (TAD) estimated from [177Lu]Lu-DOTA-TATE SPECT/CT and the therapeutic response. Method: A retrospective analysis was conducted on patients with advanced well-differentiated NETs grades 1–3 who underwent PRRT and exhibited greater uptake than liver on pre-therapeutic [68Ga]Ga-DOTA-TOC PET/CT. Target lesions were selected based on the RECIST 1.1 and PERCIST 1.0 criteria using [177Lu]Lu-DOTA-TATE SPECT/CT and pre-therapeutic contrast-enhanced CT scans. For anatomical image analysis, the sum of the longest diameter (SLD) of the target lesions was measured using the RECIST 1.1 criteria for patient-based analysis and the longest diameter (LD) of the target lesion using the RECIST-L criteria for lesion-based analysis. Standardised uptake values (SUVs) were measured on SPECT/CT images, and TADs were calculated based on the SUVs. Dosimetry was performed using a single SPECT/CT imaging time point at day 4–5 post-therapy. Statistical analyses were conducted to investigate correlations and determine the target lesion responses. Results: Twenty patients with primary tumour sites and hepatic metastases were included. Fifty-five target lesions, predominantly located in the pancreas and liver, were analysed. The cumulative TAD (lesion-based analysis: r = 0.299–0.301, p = 0.025–0.027), but not the cycle 1 SUV (lesion-based analysis: r = 0.198–0.206, p = 0.131–0.147) or cycle 1 TAD (lesion-based analysis: r = 0.209–0.217, p = 0.112–0.126), exhibited a significant correlation with the change in LD of the target lesion. Binary logistic regression analysis identified the significance of the cumulative TAD in predicting disease control according to the RECIST-L criteria (odds ratio = 1.031–1.051, p = 0.024–0.026). Conclusions: The cumulative TAD estimated from [177Lu]Lu-DOTA-TATE SPECT/CT revealed a significant correlation with change in LD, which was significantly higher for the cumulative TAD than for the cycle 1 SUV or TAD. A higher cumulative TAD was associated with disease control in the target lesion. However, considering the limitations inherent to a confined sample size, careful interpretation of these findings is required. Estimation of the cumulative TAD of [177Lu]Lu-DOTA-TATE therapy could guide the platform towards personalised therapy. [ABSTRACT FROM AUTHOR]

Published
2024
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