Objective: To compare digital breast tomosynthesis (DBT) and MRI as an adjunct to full-field digital mammography (FFDM) for the preoperative evaluation of women with breast cancer based on mammographic density. Materials and Methods: This retrospective study enrolled 280 patients with breast cancer who had undergone FFDM, DBT, and MRI for preoperative local tumor staging. Three radiologists independently sought the index cancer and additional ipsilateral and contralateral breast cancers using either FFDM alone, DBT plus FFDM, or MRI plus FFDM. Diagnostic performances across the three radiologists were compared among the reading modes in all patients and subgroups with dense (n = 186) and non-dense breasts (n = 94) according to mammographic density. Results: Of 280 patients, 46 (16.4%) had 48 additional (39 ipsilateral and nine contralateral) cancers in addition to the index cancer. For index cancers, both DBT plus FFDM and MRI plus FFDM showed sensitivities of 100% in the non-dense group. In the dense group, DBT plus FFDM showed lower sensitivity than that of MRI plus FFDM (94.6% vs. 99.6%, p < 0.001). For additional ipsilateral cancers, DBT plus FFDM showed specificity and positive predictive value (PPV) of 100% in the non-dense group, but sensitivity and negative predictive value (NPV) were not statistically different from those of MRI plus FFDM (p > 0.05). In the dense group, DBT plus FFDM showed higher specificity (98.2% vs. 94.1%, p = 0.005) and PPV (83.1% vs. 65.4%; p = 0.036) than those of MRI plus FFDM, but lower sensitivity (59.9% vs. 75.3%; p = 0.049). For contralateral cancers, DBT plus FFDM showed higher specificity than that of MRI plus FFDM (99.0% vs. 96.7%, p = 0.014), however, the other values did not differ (all p > 0.05) in the dense group. Conclusion: DBT plus FFDM showed an overall higher specificity than that of MRI plus FFDM regardless of breast density, perhaps without substantial loss in sensitivity and NPV in the diagnosis of additional cancers. Thus, DBT may have the potential to be used as a preoperative breast cancer staging tool.

Objective: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. Materials and Methods: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. Results: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. Conclusion: DLR reconstruction provided better images than FBP and hybrid IR reconstruction.

Objective: The clinical relevance of myosteatosis has not been well evaluated in patients with pancreatic ductal adenocarcinoma (PDAC), although sarcopenia has been extensively researched. Therefore, we evaluated the prognostic value of muscle quality, including myosteatosis, in patients with resectable PDAC treated surgically. Materials and Methods: We retrospectively evaluated 347 patients with resectable PDAC who underwent curative surgery (mean age ± standard deviation, 63.6 ± 9.6 years; 202 male). Automatic muscle segmentation was performed on preoperative computed tomography (CT) images using an artificial intelligence program. A single axial image of the portal phase at the inferior endplate level of the L3 vertebra was used for analysis in each patient. Sarcopenia was evaluated using the skeletal muscle index, calculated as the skeletal muscle area (SMA) divided by the height squared. The mean SMA attenuation was used to evaluate myosteatosis. Diagnostic cutoff values for sarcopenia and myosteatosis were devised using the Contal and O'Quigley methods, and patients were classified according to normal (nMT), sarcopenic (sMT), myosteatotic (mMT), or combined (cMT) muscle quality types. Multivariable Cox regression analyses were conducted to assess the effects of muscle type on the overall survival (OS) and recurrence-free survival (RFS) after surgery. Results: Eighty-four (24.2%), 73 (21.0%), 75 (21.6%), and 115 (33.1%) patients were classified as having nMT, sMT, mMT, and cMT, respectively. Compared to nMT, mMT and cMT were significantly associated with poorer OS, with hazard ratios (HRs) of 1.49 (95% confidence interval, 1.00-2.22) and 1.68 (1.16-2.43), respectively, while sMT was not (HR of 1.40 [0.94-2.10]). Only mMT was significantly associated with poorer RFS, with an HR of 1.59 (1.07-2.35), while sMT and cMT were not. Conclusion: Myosteatosis was associated with poor OS and RFS in patients with resectable PDAC who underwent curative surgery.

Objective: To determine whether Sonazoid-enhanced ultrasound (SZUS) was noninferior to SonoVue-enhanced ultrasound (SVUS) in diagnosing hepatocellular carcinoma (HCC) using the same diagnostic criteria. Materials and Methods: This prospective, single-center, noninferiority study (NCT04847726) enrolled 105 at-risk participants (71 male; mean age ± standard deviation, 63 ± 11 years; range, 26-86 years) with treatment-naïve solid hepatic nodules (≥ 1 cm). All participants underwent same-day SZUS (experimental method) and SVUS (control method) for one representative nodule per participant. Images were interpreted by three readers (the operator and two independent readers). All malignancies were diagnosed histopathologically, while the benignity of other lesions was confirmed by follow-up stability or pathology. The primary endpoint was per-lesion diagnostic accuracy for HCC pooled across three readers using the conventional contrast-enhanced ultrasound diagnostic criteria, including arterial phase hyperenhancement followed by mild (assessed within 2 minutes after contrast injection) and late (≥ 60 seconds with a delay of 5 minutes) washout. The noninferiority delta was -10%p. Furthermore, different time delays were compared as washout criteria in SZUS, including delays of 2, 5, and > 10 minutes. Results: A total of 105 lesions (HCCs [n = 61], non-HCC malignancies [n = 19], and benign [n = 25]) were evaluated. Using the 5-minutes washout criterion, per-lesion accuracy of SZUS pooled across the three readers (72.4%; 95% confidence interval [CI], 64.1%-79.3%) was noninferior to that of SVUS (71.4%; 95% CI, 63.1%-78.6%), meeting the statistical criterion for non-inferiority (difference of 0.95%p; 95% CI, -3.8%p-5.7%p). The arterial phase hyperenhancement combined with the 5-minutes washout criterion showed the same sensitivity as that of the > 10-minutes criterion (59.0% vs. 59.0%, p = 0.989), and the specificities were not significantly different (90.9% vs. 86.4%, p = 0.072). Conclusion: SZUS was noninferior to SVUS for diagnosing HCC in at-risk patients using the same diagnostic criteria. No significant improvement in HCC diagnosis was observed by extending the washout time delay from 5 to 10 minutes.

Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). Materials and Methods: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. Results: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. Conclusion: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid development of immunotherapeutic agents, from immune checkpoint inhibitors to chimeric antigen receptor-T cells, with which many radiologists may not be familiar, and the atypical responses to therapy, such as pseudoprogression and hyperprogression. Therefore, new response assessment methods such as immune response assessment, functional/molecular imaging biomarkers, and artificial intelligence (including radiomics and machine learning approaches) have been developed and investigated. Radiologists should be aware of recent trends in immunotherapy development and new response assessment methods.

Objective: To evaluate the ultrasonography (US) features for diagnosing metastasis in cervical lymph nodes (LNs) in patients with thyroid cancer and compare the US classification of risk of LN metastasis between European and Korean guidelines. Materials and Methods: From January 2014 to December 2018, US-guided fine-needle aspiration was performed on 836 LNs from 714 patients for the preoperative nodal staging of thyroid cancer. The US features of LNs were retrospectively reviewed for the following features: size, presence of hilum, margin, orientation, cystic change, punctate echogenic foci (PEF), large echogenic foci, eccentric cortical thickening, abnormal vascularity, and cortical hyperechogenicity. A multiple logistic regression analysis was performed to identify the independent US features for the diagnosis of metastatic LNs. The diagnostic performance of independent US features was subsequently evaluated. LNs were categorized according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and European Thyroid Association (ETA) guidelines, and the correlation between the two sets of classifications was assessed. Results: Absence of the hilum, presence of cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features of metastatic LNs. Cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity showed high specificity (86.8%-99.6%). The absence of the hilum had the highest sensitivity yet low specificity (66.4%). When LNs were classified according to the ETA guidelines and K-TIRADS, they yielded similar categorizations of malignancy risks and were strongly correlated (Spearman coefficient, 0.9766 [95% confidence interval, 0.973-0.979]). According to the ETA guidelines, 9.8% (82/836) of LNs were classified as "not specified." Conclusion: Absence of hilum, cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features suggestive of metastatic LNs in thyroid cancer. Both K-TIRADS and the ETA guidelines provided similar risk stratification for metastatic LNs with a high correlation; however, the ETA guidelines failed to classify 9.8% of LNs into a specific risk stratum. These results may provide a basis for revising LN classification in future guidelines.