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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.

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.

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.

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.

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.