7 results on '"Lin, Yannan"'
Search Results
2. Using Discrete Event Simulation to Design and Assess an AI-aided Workflow for Same-day Diagnostic Testing of Women Undergoing Breast Screening.
- Author
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Lin, Yannan, Hoyt, Anne C, Manuel, Vladimir G, Inkelas, Moira, and Hsu, William
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Health Services and Systems ,Health Sciences ,Biomedical Imaging ,Cancer ,Prevention ,Breast Cancer ,Women's Health ,Health Services ,Clinical Research ,4.2 Evaluation of markers and technologies - Abstract
The process of patients waiting for diagnostic examinations after an abnormal screening mammogram is inefficient and anxiety-inducing. Artificial intelligence (AI)-aided interpretation of screening mammography could reduce the number of recalls after screening. We proposed a same-day diagnostic workup to alleviate patient anxiety by employing an AI-aided interpretation to reduce unnecessary diagnostic testing after an abnormal screening mammogram. However, the potential unintended consequences of introducing this workflow in a high-volume breast imaging center are unknown. Using discrete event simulation, we observed that implementing the AI-aided screening mammogram interpretation and same-day diagnostic workflow would reduce daily patient volume by 4%, increase the time a patient would be at the clinic by 24%, and increase waiting times by 13-31%. We discuss how changing the hours of operation and introducing new imaging equipment and personnel may alleviate these negative impacts.
- Published
- 2024
3. Patient Adherence to Lung CT Screening Reporting & Data System–Recommended Screening Intervals in the United States: A Systematic Review and Meta-Analysis
- Author
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Lin, Yannan, Fu, Mingzhou, Ding, Ruiwen, Inoue, Kosuke, Jeon, Christie Y, Hsu, William, Aberle, Denise R, and Prosper, Ashley Elizabeth
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Biomedical and Clinical Sciences ,Clinical Sciences ,Rare Diseases ,Prevention ,Lung ,Cancer ,Good Health and Well Being ,Early Detection of Cancer ,Humans ,Lung Neoplasms ,Patient Compliance ,Tomography ,X-Ray Computed ,United States ,Lung cancer screening ,Patient adherence ,Lung-RADS ,Systematic review ,Meta-analysis ,Cardiorespiratory Medicine and Haematology ,Oncology and Carcinogenesis ,Oncology & Carcinogenesis ,Clinical sciences ,Oncology and carcinogenesis - Abstract
Lung cancer screening (LCS) is effective in reducing mortality, particularly when patients adhere to follow-up recommendations standardized by the Lung CT Screening Reporting & Data System (Lung-RADS). Nevertheless, patient adherence to recommended intervals varies, potentially diminishing benefit from screening. We conducted a systematic review and meta-analysis of patient adherence to Lung-RADS-recommended screening intervals. We systematically searched MEDLINE, EMBASE, Web of Science, the Cochrane Central Register of Controlled Trials, and major radiology and oncology conference archives between April 28, 2014, and December 17, 2020. Eligible studies mentioned patient adherence to the recommendations of Lung-RADS. The review protocol was registered with PROSPERO (CRD42020189326). We identified 24 eligible studies for qualitative summary, of which 21 were suitable for meta-analysis. The pooled adherence rate was 57% (95% confidence interval: 46%-69%) for defined adherence (e.g., an annual incidence screen was performed within 15 mo) among 6689 patients and 65% (95% confidence interval: 55%-75%) for anytime adherence among 5085 patients. Large heterogeneity in adherence rates between studies was observed (I2 = 99% for defined adherence, I2 = 98% for anytime adherence). Heterogeneous adherence rates were associated with Lung-RADS scores, with significantly higher adherence rates among Lung-RADS 3 to 4 than Lung-RADS 1 to 2 (p < 0.05). Patient adherence to Lung-RADS-recommended screening intervals is suboptimal across clinical LCS programs in the United States, especially among patients with results of Lung-RADS categories 1 to 2. To improve adherence rates, future research may focus on implementing tailored interventions after identifying barriers to LCS. We also propose a minimum standardized set of data elements for future pooled analyses of LCS adherence on the basis of our findings.
- Published
- 2022
4. EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography.
- Author
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Lin, Yannan, Wei, Leihao, Han, Simon X, Aberle, Denise R, and Hsu, William
- Subjects
computed tomography ,computer-aided diagnosis ,deep learning ,pulmonary nodule classification ,pulmonary nodule detection ,Bioengineering ,Biomedical Imaging ,Cancer ,Good Health and Well Being - Abstract
We present an interpretable end-to-end computer-aided detection and diagnosis tool for pulmonary nodules on computed tomography (CT) using deep learning-based methods. The proposed network consists of a nodule detector and a nodule malignancy classifier. We used RetinaNet to train a nodule detector using 7,607 slices containing 4,234 nodule annotations and validated it using 2,323 slices containing 1,454 nodule annotations drawn from the LIDC-IDRI dataset. The average precision for the nodule class in the validation set reached 0.24 at an intersection over union (IoU) of 0.5. The trained nodule detector was externally validated using a UCLA dataset. We then used a hierarchical semantic convolutional neural network (HSCNN) to classify whether a nodule was benign or malignant and generate semantic (radiologist-interpretable) features (e.g., mean diameter, consistency, margin), training the model on 149 cases with diagnostic CTs collected from the same UCLA dataset. A total of 149 nodule-centered patches from the UCLA dataset were used to train the HSCNN. Using 5-fold cross validation and data augmentation, the mean AUC and mean accuracy in the validation set for predicting nodule malignancy achieved 0.89 and 0.74, respectively. Meanwhile, the mean accuracy for predicting nodule mean diameter, consistency, and margin were 0.59, 0.74, and 0.75, respectively. We have developed an initial end-to-end pipeline that automatically detects nodules ≥ 5 mm on CT studies and labels identified nodules with radiologist-interpreted features automatically.
- Published
- 2020
5. USING A GENERATIVE ADVERSARIAL NETWORK FOR CT NORMALIZATION AND ITS IMPACT ON RADIOMIC FEATURES
- Author
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Wei, Leihao, Lin, Yannan, Hsu, William, and IEEE
- Subjects
lung cancer ,radiomics ,generative adversarial networks ,deep neural networks ,denoising ,eess.IV ,cs.CV ,cs.LG ,stat.ML - Abstract
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifyingand classifying potentially malignant pulmonary nodules on chest CT scans usingmorphology and texture-based (radiomic) features. However, radiomic featuresare sensitive to differences in acquisitions due to variations in dose levelsand slice thickness. This study investigates the feasibility of generating anormalized scan from heterogeneous CT scans as input. We obtained projectiondata from 40 low-dose chest CT scans, simulating acquisitions at 10%, 25% and50% dose and reconstructing the scans at 1.0mm and 2.0mm slice thickness. A 3Dgenerative adversarial network (GAN) was used to simultaneously normalizereduced dose, thick slice (2.0mm) images to normal dose (100%), thinner slice(1.0mm) images. We evaluated the normalized image quality using peaksignal-to-noise ratio (PSNR), structural similarity index (SSIM) and LearnedPerceptual Image Patch Similarity (LPIPS). Our GAN improved perceptualsimilarity by 35%, compared to a baseline CNN method. Our analysis also showsthat the GAN-based approach led to a significantly smaller error (p-value <0.05) in nine studied radiomic features. These results indicated that GANscould be used to normalize heterogeneous CT images and reduce the variabilityin radiomic feature values.
- Published
- 2020
6. USING A GENERATIVE ADVERSARIAL NETWORK FOR CT NORMALIZATION AND ITS IMPACT ON RADIOMIC FEATURES
- Author
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Wei, Leihao, Lin, Yannan, and Hsu, William
- Subjects
lung cancer ,radiomics ,generative adversarial networks ,deep neural networks ,denoising ,eess.IV ,cs.CV ,cs.LG ,stat.ML - Abstract
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifyingand classifying potentially malignant pulmonary nodules on chest CT scans usingmorphology and texture-based (radiomic) features. However, radiomic featuresare sensitive to differences in acquisitions due to variations in dose levelsand slice thickness. This study investigates the feasibility of generating anormalized scan from heterogeneous CT scans as input. We obtained projectiondata from 40 low-dose chest CT scans, simulating acquisitions at 10%, 25% and50% dose and reconstructing the scans at 1.0mm and 2.0mm slice thickness. A 3Dgenerative adversarial network (GAN) was used to simultaneously normalizereduced dose, thick slice (2.0mm) images to normal dose (100%), thinner slice(1.0mm) images. We evaluated the normalized image quality using peaksignal-to-noise ratio (PSNR), structural similarity index (SSIM) and LearnedPerceptual Image Patch Similarity (LPIPS). Our GAN improved perceptualsimilarity by 35%, compared to a baseline CNN method. Our analysis also showsthat the GAN-based approach led to a significantly smaller error (p-value <0.05) in nine studied radiomic features. These results indicated that GANscould be used to normalize heterogeneous CT images and reduce the variabilityin radiomic feature values.
- Published
- 2020
7. Characterizing Patient Adherence to Lung Cancer Screening Guidelines
- Author
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Lin, Yannan
- Subjects
Biomedical engineering ,Adherence ,Lung Cancer ,Screening - Abstract
Lung cancer is the leading cause of cancer-related death in both sexes. Large, randomized clinical trials have demonstrated that low-dose computed tomography screening reduces mortality from lung cancer, as opposed to chest X-ray or no screening, when participants adhere to follow-up recommendations. However, low adherence rates in post-trial clinical lung cancer screening (LCS) programs have been reported across the United States (US). Low adherence to LCS in real-world clinical practice diminishes the mortality benefit of annual screening derived from clinical trials. Thus far, limited studies have examined factors affecting the patient’s decision (not) to adhere to screening guidelines. This dissertation examines the factors that may predict patient non-adherence to LCS recommendations. First, we performed a systematic review and meta-analysis of 24 studies published between 2014 and 2020 that mentioned adherence to Lung-RADS recommendations, identifying factors contributing to adherence rates. Second, using the Carter-Harris conceptual model, which enumerates psychosocial variables (e.g., smoking stigma, cancer fear, cancer fatalism) related to LCS participation and adherence, we examined these variables’ availability and completeness in our medical records. Next, using a subset of variables where data were consistently available, we identified factors of non-adherence over multiple screening time points using logistic regression and mixed effects models. Lastly, we used statistical and machine learning-based methods to examine how well we could predict patient non-adherence using longitudinal data across three screening time points. This dissertation advances our understanding of factors contributing to patient non-adherence to LCS recommendations. It provides a basis for identifying patient groups that could benefit from individualized interventions to improve LCS adherence.
- Published
- 2023
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