Thymic Imaging Pitfalls and Strategies for Optimized Diagnosis.

Thymic imaging is challenging because the imaging appearance of a variety of benign and malignant thymic conditions are similar. CT is the most commonly used modality for mediastinal imaging, while MRI and fluorine 18 fluorodeoxyglucose (FDG) PET/CT are helpful when they are tailored to the correct indication. Each of these imaging modalities has limitations and technical pitfalls that may lead to an incorrect diagnosis and mismanagement. CT may not be sufficient for the characterization of cystic thymic processes and differentiation between thymic hyperplasia and thymic tumors. MRI can be used to overcome these limitations but is subject to other potential pitfalls such as an equivocal decrease in signal intensity at chemical shift imaging, size limitations, unusual signal intensity for cysts, subtraction artifacts, pseudonodularity on T2-weighted MR images, early imaging misinterpretation, flow and spatial resolution issues hampering assessment of local invasion, and the overlap of apparent diffusion coefficients between malignant and benign thymic entities. FDG PET/CT is not routinely indicated due to some overlap in FDG uptake between thymomas and benign thymic processes. However, it is useful for staging and follow-up of aggressive tumors (eg, thymic carcinoma), particularly for detection of occult metastatic disease. Pitfalls in imaging after treatment of thymic malignancies relate to technical challenges such as postthymectomy sternotomy streak metal artifacts, differentiation of postsurgical thymic bed changes from tumor recurrence, or human error with typical "blind spots" for identification of metastatic disease. Understanding these pitfalls enables appropriate selection of imaging modalities, improves diagnostic accuracy, and guides patient treatment. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Liver Attenuation Assessment in Reduced Radiation Chest Computed Tomography.

OBJECTIVE: This study aimed to evaluate the reliability of liver and spleen Hounsfield units (HU) measurements in reduced radiation computed tomography (RRCT) of the chest within the sub-millisievert range. METHODS: We performed a prospective, institutional review board-approved study of accrued patients who underwent unenhanced normal-dose chest CT (NDCT) and with an average radiation dose of less than 5% of NDCT. In-house artificial intelligence-based denoising methods produced 2 denoised RRCT (dRRCT) series. Hepatic and splenic attenuations were measured on all 4 series: NDCT, RRCT, dRRCT1, and dRRCT2. Statistical analyses assessed the differences between the HU measurements of the liver and spleen in RRCTs and NDCT. As a test case, we assessed the performance of RRCTs for fatty liver detection, considering NDCT to be the reference standard. RESULTS: Wilcoxon test compared liver and spleen attenuation in the 72 patients included in our cohort. The liver attenuation in NDCT (median, 59.38 HU; interquartile range, 55.00-66.06 HU) was significantly different from the attenuation in RRCT, dRRCT1, and dRRCT2 (median, 63.63, 42.00, and 33.67 HU; interquartile range, 56.19-67.19, 37.33-45.83, and 30.33-38.50 HU, respectively), all with a P value <0.01. Six patients (8.3%) were considered to have fatty liver on NDCT. The specificity, sensitivity, and accuracy of fatty liver detection by RRCT were greater than 98.5%, 50%, and 94.3%, respectively. CONCLUSIONS: Attenuation measurements were significantly different between NDCT and RRCTs, but may still have diagnostic value in appreciating hepatosteastosis. Abdominal organ attenuation on RRCT protocols may differ from attenuation on NDCT and should be validated when new low-dose protocols are used.

Ultra-Low Dose Chest CT with Denoising for Lung Nodule Detection.

BACKGROUND: Medical imaging and the resultant ionizing radiation exposure is a public concern due to the possible risk of cancer induction. OBJECTIVES: To assess the accuracy of ultra-low-dose (ULD) chest computed tomography (CT) with denoising versus normal dose (ND) chest CT using the Lung CT Screening Reporting and Data System (Lung-RADS). METHODS: This prospective single-arm study comprised 52 patients who underwent both ND and ULD scans. Subsequently AI-based denoising methods were applied to produce a denoised ULD scan. Two chest radiologists independently and blindly assessed all scans. Each scan was assigned a Lung-RADS score and grouped as 1 + 2 and 3 + 4. RESULTS: The study included 30 men (58%) and 22 women (42%); mean age 69.9 ± 9 years (range 54-88). ULD scan radiation exposure was comparable on average to 3.6-4.8% of the radiation depending on patient BMI. Denoising increased signal-to-noise ratio by 27.7%. We found substantial inter-observer agreement in all scans for Lung-RADS grouping. Denoised scans performed better than ULD scans when negative likelihood ratio (LR-) was calculated (0.04--0.08 vs. 0.08-0.12). Other than radiation changes, diameter measurement differences and part-solid nodules misclassification as a ground-glass nodule caused most Lung-RADS miscategorization. CONCLUSIONS: When assessing asymptomatic patients for pulmonary nodules, finding a negative screen using ULD CT with denoising makes it highly unlikely for a patient to have a pulmonary nodule that requires aggressive investigation. Future studies of this technique should include larger cohorts and be considered for lung cancer screening as radiation exposure is radically reduced.

Retrospectively assessing evaluation and management of artificial-intelligence detected nodules on uninterpreted chest radiographs in the era of radiologists shortage.

PURPOSE: High volumes of chest radiographs (CXR) remain uninterpreted due to severe shortage of radiologists. These CXRs may be informally reported by non-radiologist physicians, or not reviewed at all. Artificial intelligence (AI) software can aid lung nodule detection. Our aim was to assess evaluation and management by non-radiologists of uninterpreted CXRs with AI detected nodules, compared to retrospective radiology reports. MATERIALS AND METHODS: AI detected nodules on uninterpreted CXRs of adults, performed 30/6/2022-31/1/2023, were evaluated. Excluded were patients with known active malignancy and duplicate CXRs of the same patient. The electronic medical records (EMR) were reviewed, and the clinicians' notes on the CXR and AI detected nodule were documented. Dedicated thoracic radiologists retrospectively interpreted all CXRs, and similarly to the clinicians, they had access to the AI findings, prior imaging and EMR. The radiologists' interpretation served as the ground truth, and determined if the AI-detected nodule was a true lung nodule and if further workup was required. RESULTS: A total of 683 patients met the inclusion criteria. The clinicians commented on 386 (56.5%) CXRs, identified true nodules on 113 CXRs (16.5%), incorrectly mentioned 31 (4.5%) false nodules as real nodules, and did not mention the AI detected nodule on 242 (35%) CXRs, of which 68 (10%) patients were retrospectively referred for further workup by the radiologist. For 297 patients (43.5%) there were no comments regarding the CXR in the EMR. Of these, 77 nodules (11.3%) were retrospectively referred for further workup by the radiologist. CONCLUSION: AI software for lung nodule detection may be insufficient without a formal radiology report, and may lead to over diagnosis or misdiagnosis of nodules.

A Novel Artificial Intelligence Based Denoising Method for Ultra-Low Dose CT Used for Lung Cancer Screening.

RATIONALE AND OBJECTIVES: To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer. MATERIALS AND METHODS: This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images. RESULTS: ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p = 0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p = 0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC = 0.924), dULD (ICC = 0.903), and for ULD (ICC = 0.817) scans. CONCLUSION: A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.

Magnetic resonance imaging for prospective assessment of local recurrence of non-small cell lung cancer after stereotactic body radiation therapy.

OBJECTIVES: To evaluate multi-parametric MRI for distinguishing stereotactic body radiation therapy (SBRT) induced pulmonary fibrosis from local recurrence (LR). MATERIALS AND METHODS: SBRT treated non-small cell lung cancer (NSCLC) patients suspected of LR by conventional imaging underwent MRI: T2 weighted, diffusion weighted imaging, dynamic contrast enhancement (DCE) with a 5-minute delayed sequence. MRI was reported as high or low suspicion of LR. Follow-up imaging ≥12 months or biopsy defined LR status as proven LR, no-LR or not-verified. RESULTS: MRI was performed between 10/2017 and 12/2021, at a median interval of 22.5 (interquartile range 10.5-32.75) months after SBRT. Of the 20 lesions in 18 patients: 4 had proven LR, 10 did not have LR and 6 were not verified for LR due to subsequent additional local and/or systemic therapy. MRI correctly identified as high suspicion LR in all proven LR lesions and low suspicion LR in all confirmed no-LR lesions. All proven LR lesions (4/4) showed heterogeneous enhancement and heterogeneous T2 signal, as compared to the proven no-LR lesions in which 7/10 had homogeneous enhancement and homogeneous T2 signal. DCE kinetic curves could not predict LR status. Although lower apparent diffusion coefficient (ADC) values were seen in proven LR lesions, no absolute cut-off ADC value could determine LR status. CONCLUSION: In this pilot study of NSCLC patients after SBRT, multi-parametric chest MRI was able to correctly determine LR status, with no single parameter being diagnostic by itself. Further studies are warranted.

Effects of EGFR driver mutations on pathologic regression in resectable locally advanced non-small cell lung cancer treated with neoadjuvant chemoradiation and completion surgery.

OBJECTIVE: We hypothesized that driver mutations in epidermal growth factor receptor (EGFR) are associated with decreased pathologic response to neoadjuvant chemoradiation (NA-ChRT) in locally advanced non-small cell lung cancer (LA-NSCLC). METHODS: Patients with Stage IIB-IIIA NSCLC treated with NA-ChRT, completion surgery, and underwent molecular profile testing were identified in a lung cancer database. Pathologic response was quantified using: (i) major pathologic response (MPR), (ii) complete pathologic response (pCR), and (iii) mean residual viable tumor cells (MRTC). Two groups were formed based on the presence or absence of driver mutations. Clinical and pathological correlations between the groups were studied. RESULTS: Forty-seven patients underwent tumor molecular profile testing, NA-ChRT, and completion surgery. Compared to the no-driver mutation group, the driver mutation group had lower MPR (23% vs 71%, p = 0.003), pCR (0% vs 26%, p = 0.02), and higher MRTC (43.4% vs 15.8%, p = 0.009). Univariate analysis showed an increased MPR rate for smokers, squamous cell histology, ChRT-surgery interval >65 days, and no-driver mutations. Multivariate analysis showed that only no-driver mutations (OR 0.39, p = 0.02) remained significant for MPR. PD-L1 status did not affect MPR. At 2 years, the driver mutation group had lower rates of local control (Hazard ration [HR] 0.67, p = 0.17) and disease-free survival (HR 0.5, p = 0.001). Overall survival was similar for both groups (HR = 1.04, p = 0.86). CONCLUSION: Following 60 Gray NA-ChRT, tumors with a driver mutation had lower MPR and pCR rates than tumors without a driver mutation. PD-L1 was not associated with tumor regression. ADVANCES IN KNOWLEDGE: Patients with resectable LA-NSCLC and an EGFR driver mutation treated with neoadjuvant-ChRT and completion surgery have reduced pathologic regression, lower local control rates, and shorter disease-free survival than patients without a driver mutation. Evaluation of molecular testing should be introduced in LA-NSCLC intended for prognostication and treatment decisions.

Gastric Banding: Complications Identified by CT.

PURPOSE: Laparoscopic adjustable gastric banding (LAGB) used to be a common procedure at the turn of the century and is still frequently encountered on CT scans in common clinical practice. Our aim is to present the frequency and spectrum of complication associated with LAGB, as observed in CT. MATERIALS AND METHODS: After approval of our institutional review board, a retrospective search for LAGB in CT interpretations using the term "band" between December 2011 and April 2017 was conducted. CT scans were reviewed to identify complications. The findings were divided into two groups: symptomatic, in which the complications caused acute symptoms for which CT scans were conducted, and incidental, in which complications were incidentally identified. The frequency of complications was calculated. RESULTS: We identified 160 patients who underwent LAGB and performed a CT scan. Complications were identified in 69/160 (43.1%) patients, with a total of 83 findings: 47/160 (29.4%) esophageal dilatation, 13/160 (8.2%) pulmonary complications, 6/160 (3.8%) abdominal abscesses, 5/160 (3.1%) small bowel obstructions, 4/160 (2.5%) intragastric band erosions, 4/160 (2.5%) tube disconnections, 3/160 (1.9%) port site and tube course infections, and 1/160 (0.6%) small pouch bezoars. When compared with patients' referral notes, 38/83 (45.8%) of the findings were associated with acute symptoms, whereas 45/83 (54.2%) of the findings were incidental. Eighteen percent of the incidental complications were clinically important. CONCLUSION: Complications were found in 43% of CT scans of patients who underwent LAGB; less than half of the findings were symptomatic. Some of the incidentally identified complications had substantial clinical importance.