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Objectives: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. Methods: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. Results: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. Conclusions: DLCT has added value for STAS prediction in lung adenocarcinoma. Clinical relevance statement: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. Key Points: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces. [ABSTRACT FROM AUTHOR]

Objectives: Invasive adenocarcinomas (IADs) have been identified among nonsolid nodules (NSNs) assigned as Lung Imaging Reporting and Data System (Lung-RADS) category 2. This study used visual assessment for differentiating IADs from noninvasive lesions (NILs) in this category. Methods: This retrospective study included 222 patients with 242 NSNs, which were resected after preoperative computed tomography (CT)–guided dye localization. Visual assessment was performed by using the lung and bone window (BW) settings to classify NSNs into BW-visible (BWV) and BW-invisible (BWI) NSNs. In addition, nodule size, shape, border, CT attenuation, and location were evaluated and correlated with histopathological results. Logistic regression was performed for multivariate analysis. A p value of < 0.05 was considered statistically significant. Results: A total of 242 NSNs (mean diameter, 7.6 ± 2.8 mm), including 166 (68.6%) BWV and 76 (31.4%) BWI NSNs, were included. IADs accounted for 31% (75) of the nodules. Only 4 (5.3%) IADs were identified in the BWI group and belonged to the lepidic-predominant (n = 3) and acinar-predominant (n = 1) subtypes. In univariate analysis for differentiating IADs from NILs, the nodule size, shape, CT attenuation, and visual classification exhibited statistical significance. Nodule size and visual classification were the significant predictors for IAD in multivariate analysis with logistic regression (p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of visual classification in IAD prediction were 94.7%, 43.1%, 42.8%, and 94.7%, respectively. Conclusions: The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs. Clinical relevance statement: The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions. Key Points: • Evidence has shown the presence of lung adenocarcinoma in Lung-RADS category 2 nonsolid nodules. • Nonsolid nodules are classified into the bone window–visible and the bone window–invisible nonsolid nodules, and this classification differentiates invasive adenocarcinoma from noninvasive lesions. • The Lung-RADS category 2 nonsolid nodules are unlikely invasive adenocarcinoma if they show nonvisualization in the bone window. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas. Methods: Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression. Results: In 681 patients (median age, 65 years [interquartile range, 58–71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3–71.9%; specificity, 71.6–88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43–0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84–1.92 and 0.63–1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86–2.42 and 0.67–1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07–3.07; p = 0.03). Conclusion: The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome. Clinical relevance statement: Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability. Key Points: • Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. • Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. • Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors. [ABSTRACT FROM AUTHOR]

This paper describes the spectrum of imaging features of oesophageal adenocarcinoma seen using high-resolution T2-weighted (T2W) magnetic resonance imaging (MRI). Thirty-nine patients with biopsy-proven oesophageal adenocarcinoma were scanned using an external surface coil. A sagittal T2W sequence was used to localise the tumour and to plan axial images perpendicular to the tumour. Fast spin-echo (FSE) T2W axial sequence parameters were: TR/TE, 3,300-5,000 ms/120-80 ms; field of view (FOV) 225 mm, matrix 176x512(reconstructed) mm to 256x224 mm, giving an in-plane resolution of between 1.28x0.44 mm and 0.88x1.00 mm, with 3-mm slice thickness. Thirty-three patients underwent resection and the MR images were compared with the histological whole-mount sections. There were four T1, 12 T2, and 17 T3 tumours. The T2W high-resolution MRI sequences produced detailed images of the oesophageal wall and surrounding structures. Analysis of the imaging appearances for different tumour T stages enabled the development of imaging criteria for local staging of oesophageal cancer using high-resolution MRI. Our study illustrates the spectrum of appearances of oesophageal cancer on T2W high-resolution MRI, and using the criteria established in this study, demonstrates the potential of this technique as an alternative non-invasive method for local staging for oesophageal cancer. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was thus to compare capabilities for quantitative differentiation of non- and minimally invasive adenocarcinomas from other of pulmonary MRIs with ultra-short TE (UTE) obtained with single- and dual-echo techniques (UTE-MRISingle and UTE-MRIDual) and thin-section CT for stage IA lung cancer patients. Methods: Ninety pathologically diagnosed stage IA lung cancer patients who underwent thin-section standard-dose CT, UTE-MRISingle, and UTE-MRIDual, surgical treatment and pathological examinations were included in this retrospective study. The largest dimension (Dlong), solid portion (solid Dlong), and consolidation/tumor (C/T) ratio of each nodule were assessed. Two-tailed Student's t-tests were performed to compare all indexes obtained with each method between non- and minimally invasive adenocarcinomas and other lung cancers. Receiver operating characteristic (ROC)-based positive tests were performed to determine all feasible threshold values for distinguishing non- or minimally invasive adenocarcinoma (MIA) from other lung cancers. Sensitivity, specificity, and accuracy were then compared by means of McNemar's test. Results: Each index showed significant differences between the two groups (p < 0.0001). Specificities and accuracies of solid Dlong for UTE-MRIDual2nd echo and CTMediastinal were significantly higher than those of solid Dlong for UTE-MRISingle and UTE-MRIDual1st echo and all C/T ratios except CTMediastinal (p < 0.05). Moreover, the specificities and accuracies of solid Dlong and C/T ratio were significantly higher than those of Dlong for each method (p < 0.05). Conclusion: Pulmonary MRI with UTE is considered at least as valuable as thin-section CT for quantitative differentiation of non- and minimally invasive adenocarcinomas from other stage IA lung cancers. Clinical relevance statement: Pulmonary MRI with UTE's capability for quantitative differentiation of non- and minimally invasive adenocarcinomas from other lung cancers in stage IA lung cancer patients is equal or superior to that of thin-section CT. Key Points: • Correlations were excellent for pathologically examined nodules with the largest dimensions (Dlong) and a solid component (solid Dlong) for all indexes (0.95 ≤ r ≤ 0.99, p < 0.0001). • Pathologically examined Dlong and solid Dlong obtained with all methods showed significant differences between non- and minimally invasive adenocarcinomas and other lung cancers (p < 0.0001). • Solid tumor components are most accurately measured by UTE-MRIDual2nd echo and CTMediastinal, whereas the ground-glass component is imaged by UTE-MRIDual1st echo and CTlung with high accuracy. UTE-MRIDual predicts tumor invasiveness with 100% sensitivity and 87.5% specificity at a C/T threshold of 0.5. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19–9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC). Methods: A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014–July 2019) and validation cohorts (n = 47, August 2019–December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan–Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model. Results: The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort). Conclusions: The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making. Clinical relevance statement: We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments. Key Points: • Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19–9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models. [ABSTRACT FROM AUTHOR]

Objectives: To determine informational CT findings for distinguishing autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC) and to review their diagnostic accuracy. Methods: A systematic and detailed literature review was performed through PubMed, EMBASE, and the Cochrane library. Similar descriptors to embody the identical image finding were labeled as a single CT characteristic. We calculated the pooled diagnostic odds ratios (DORs) of each CT characteristic using a bivariate random-effects model. Results: A total of 145 various descriptors from 15 studies (including 562 AIP and 869 PDAC patients) were categorized into 16 CT characteristics. According to the pooled DOR, 16 CT characteristics were classified into three groups (suggesting AIP, suggesting PDAC, and not informational). Seven characteristics suggesting AIP were diffuse pancreatic enlargement (DOR, 48), delayed homogeneous enhancement (DOR, 46), capsule-like rim (DOR, 34), multiple pancreatic masses (DOR, 16), renal involvement (DOR, 15), retroperitoneal fibrosis (DOR, 13), and bile duct involvement (DOR, 8). Delayed homogeneous enhancement showed a pooled sensitivity of 83% and specificity of 85%. The other six characteristics showed relatively low sensitivity (12–63%) but high specificity (93–99%). Four characteristics suggesting PDAC were discrete pancreatic mass (DOR, 23), pancreatic duct cutoff (DOR, 16), upstream main pancreatic duct dilatation (DOR, 8), and upstream parenchymal atrophy (DOR, 7). Conclusion: Eleven CT characteristics were informational to distinguish AIP from PDAC. Diffuse pancreatic enlargement, delayed homogeneous enhancement, and capsule-like rim suggested AIP with the highest DORs, whereas discrete pancreatic mass suggested PDAC. However, pooled sensitivities of informational CT characteristics were moderate. Clinical relevance statement: This meta-analysis underscores eleven distinctive CT characteristics that aid in differentiating autoimmune pancreatitis from pancreatic adenocarcinoma, potentially preventing misdiagnoses in patients presenting with focal/diffuse pancreatic enlargement. Key Points: • Diffuse pancreatic enlargement (pooled diagnostic odds ratio [DOR], 48), delayed homogeneous enhancement (46), and capsule-like rim (34) were CT characteristics suggesting autoimmune pancreatitis. • The CT characteristics suggesting autoimmune pancreatitis, except delayed homogeneous enhancement, had a general tendency to show relatively low sensitivity (12–63%) but high specificity (93–99%). • Discrete pancreatic mass (pooled diagnostic odds ratio, 23) was the CT characteristic suggesting pancreatic ductal adenocarcinoma with the highest pooled DORs. [ABSTRACT FROM AUTHOR]

Objectives: To determine the sensitivity of contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC) and identify factors associated with false negatives (FNs). Methods: Patients diagnosed with PDAC in 2014–2015 were retrospectively identified by a cancer registry. CECTs performed during the diagnostic interval were retrospectively classified as true positive (TP), indeterminate, or FN. Sensitivity TP/(TP+FN) was calculated for all CECTs and the following subgroups: protocol (uniphasic vs. biphasic); tumor size (≤ 2 cm vs. > 2 cm); and resectability (potentially resectable vs. unresectable). Multivariate logistic regression was performed to assess which of the following factors were associated with FN: clinical suspicion of PDAC; size >2 cm; presence of metastases; protocol; isoattenuating tumor; and potentially resectable disease on imaging. Results: In total, 176 CECTs (127 uniphasic; 49 biphasic) in 154 patients (90 men, mean age 72 ± 11 years) were included. Sensitivity was 125/149 (83.9%) overall and 87/106 (82.1%) and 38/43 (88.4%) for uniphasic and biphasic protocols, respectively. Sensitivity was decreased for tumors ≤ 2 cm (45.4% vs. 90.6%), no liver metastases (78.0% vs. 95.9%), and potentially resectable disease (65.3% vs. 93.0%). Factors significantly associated with FN were clinical suspicion (OR, 0.24, 95% CI: 0.07–0.75), size>2 cm (OR, 0.10, 95% CI: 0.02–0.44), absence of liver metastases (OR, 4.94, 95% CI: 1.29–22.99), and potentially resectable disease (OR, 4.13, 95% CI: 1.07–16.65). Conclusions: In our population, the overall sensitivity of CECT to detect PDAC is 83.9%; however, this is substantially lower in several scenarios, including patients with potentially resectable disease. This finding has important implications for patient outcomes and efforts to maximize CECT sensitivity should be sought. Clinical relevance statement: The sensitivity of CECT to detect PDAC is significantly decreased in the setting of sub-2 cm tumors and potentially resectable disease. A dedicated biphasic pancreatic CECT protocol has higher sensitivity and should be applied in patients with suspected pancreatic disease. Key Points: • The sensitivities of contrast-enhanced CT for the detection of PDAC were 87/106 (82.1%) and 38/43 (88.4%) for uniphasic and biphasic protocols, respectively. • The sensitivity of contrast-enhanced CT was decreased for small tumors ≤ 2 cm (45.4% vs. 90.6%), if there were no liver metastases (78.0% vs. 95.9%), and with potentially resectable disease (65.3% vs. 93.0%). • Absence of liver metastases (OR, 4.94, 95% CI: 1.29–22.99) and potentially resectable disease (OR, 4.13, 95% CI: 1.07–16.65) were associated with a false--negative (FN) CT result; suspicion of malignancy on the imaging requisition (OR, 0.24, 95% CI: 0.07–0.75) and size > 2 cm (OR, 0.10, 95% CI: 0.02–0.44) were negatively associated with FN. [ABSTRACT FROM AUTHOR]

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant solid tumor that lacks early diagnostic methods. Recently, targeted immunotherapy and radiotherapy have been integrated with radionuclide-antibody conjugate drugs, which can be used for targeted diagnosis and dynamic imaging of tumors. CEACAM6 is overexpressed in pancreatic tumors and is a potential theranostic target for PDAC. We aimed to develop a novel targeted carrier for theranostics of PDAC and other solid tumors. Methods: Based on camelid heavy-chain-only antibodies, we developed a CEACAM6-targeting recombinant antibody NY004, and evaluated it as a novel antibody-carrier for imaging and therapy of cancer in tumor models. We labeled NY004 with theranostic nuclides and applied this self-developed antibody platform in diagnostic imaging and antitumor assessment in PDAC models. Results: Through microPET, IHC, and biodistribution assays, targeting and biodistribution of [89Zr]-NY004 in solid tumors including PDAC was examined, and the investigated tumors were all CEACAM6-positive malignancies. We found that NY004 was suitable for use as a drug carrier for radioimmunotheranostics. Our study showed that NY004 was characterized by high targeted uptake and a long retention time in PANC-1 tumors (up to 6 days post-injection), with good specificity and high imaging efficiency. Therapeutic evaluation of the radionuclide-labeled antibody drug [177Lu]-NY004 in PDAC tumor-bearing model revealed that NY004 had high and prolonged uptake in tumors, relatively low non-target organ uptake, and good anti-tumor efficacy. Conclusion: As a drug platform for radiotheranostics, CEACAM6-specific antibody NY004 met the requirements of easy-labeling, targeting specificity, and effective persistence in pancreatic adenocarcinoma tissues. Key Points: • [89Zr]-NY004 has good specificity and high imaging efficiency, and is characterized by high tumor-targeting uptake and a long tumor retention time as a PET molecular imaging tracer. • Therapeutic radionuclide-conjugated antibody drug [177Lu]-NY004 has high uptake and prolonged uptake duration in tumors, low non-target organ uptake, and significant tumor-inhibiting efficacy in PDAC model. • The self-developed antibody structure NY004 is a promising drug platform for radioimmunotheranostics of CEACAM6-positive tumors including pancreatic ductal adenocarcinoma. [ABSTRACT FROM AUTHOR]

Objective: To characterize intrapancreatic late enhancement (ILE) observed in the early stages of pancreatic adenocarcinoma (PAC). Methods: Among 203 patients pathologically diagnosed with PAC between October 2011 and February 2021, 32 patients with pre-diagnostic abdominal contrast-enhanced CT performed from 6 months to 5 years before the diagnosis were enrolled in this study. Indirect findings (IFs) on pre-diagnostic CT, including ILE, were evaluated and examined for various clinical data and time intervals to diagnosis (TIDs). The detected ILE was quantitatively evaluated, and the effect of ILE awareness on lesion detection by two radiologists and their interobserver agreement were assessed. Results: Among the 32 patients, 23 showed IFs. ILE was observed in 14 patients (63%), with a median TID of 17 months (interquartile ratio [IQR]: 9.3–42.3). ILE alone was observed in eight patients (35%), ILE with focal pancreatic parenchymal atrophy (FPPA) was observed in five patients (22%), and ILE with main pancreatic duct abnormalities (MPDA) was observed in one patient (4%). Pancreatic head lesions were significantly more frequent in patients with ILE alone than in patients with FPPA or MPDA (p = 0.026). The median long-axis diameters of the region with ILE and ILE-to-pancreas contrast were 10 (IQR: 5–11) mm and 24 (IQR: 17–33) HU, respectively. Awareness of ILE led observers to detect two or three more pancreatic head lesions, and interobserver agreement increased from poor agreement (k = 0.17) to moderate agreement (k = 0.55). Conclusion: ILE is a significant IF for early PAC detection. Key Points: • Intrapancreatic late enhancement (ILE) is a significant indirect finding in the early detection of pancreatic adenocarcinoma. • ILE without other indirect findings is expected to help detect pancreatic head lesions. • Image evaluation focusing on ILE can increase lesion detection and improve the interobserver agreement. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the diagnostic performance and interobserver variability in the determination of arterial invasion in pancreatic ductal adenocarcinoma (PDAC) and determine the best CT imaging criterion. Methods: We retrospectively evaluated 128 patients with PDAC (73 men and 55 women) who underwent preoperative contrast-enhanced CT. Five board-certified radiologists (expert) and four fellows (non-expert]) independently assessed the arterial invasion (celiac, superior mesenteric, splenic, and common hepatic arteries) using a 6-point score: 1, no tumor contact; 2, hazy attenuation ≤ 180°; 3, hazy attenuation > 180°; 4, solid soft tissue contact ≤ 180°; 5, solid soft tissue contact > 180°; and 6, contour irregularity. ROC analysis was performed to evaluate the diagnostic performance and determine the best diagnostic criterion for arterial invasion, with pathological or surgical findings as references. Interobserver variability was assessed using Fleiss's ĸ statistics. Results: Among the 128 patients, 35.2% (n = 45/128) received neoadjuvant treatment (NTx). Solid soft tissue contact ≤ 180° was the best diagnostic criterion for arterial invasion as defined by the Youden Index both in patients who did and did not receive NTx (sensitivity, 100% vs. 100%; specificity, 90% vs. 93%; and AUC, 0.96 vs. 0.98, respectively). Interobserver variability among the non-expert was not inferior to that among the expert (ĸ = 0.61 vs 0.61; p =.39 and ĸ = 0.59 vs 0.51; p <.001 in patients treated with and without NTx, respectively). Conclusions: Solid soft tissue contact ≤ 180° was the best diagnostic criterion for the determination of arterial invasion in PDAC. Considerable interobserver variability was seen among the radiologists. Key Points: • Solid soft tissue contact ≤ 180° was the best diagnostic criterion for the determination of arterial invasion in pancreatic ductal adenocarcinoma. • Interobserver agreement among non-expert radiologists was almost comparable to that among expert radiologists. [ABSTRACT FROM AUTHOR]

Objectives: To build and validate a multi-parametric MRI (mpMRI)-based radiomics nomogram for early prediction of treatment response to neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer. Methods: Baseline MRI were retrospectively enrolled from 141 patients with gastric adenocarcinoma who received NAC followed by radical gastrectomy. According to pathologic response of tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) and further divided into a training (n = 85) and validation dataset (n = 56). Radiomics score (Radscore) were built from T2WI, ADC, and venous phase of dynamic-contrasted-enhanced MR imaging. Clinical information, laboratory indicators, MRI parameters, and follow-up data were also recorded. According to multivariable regression analysis, an mpMRI radiomics nomogram was built and its predictive ability was evaluated by ROC analysis. Decision curve analysis was applied to evaluate the clinical usefulness. Kaplan-Meier survival curves based on the nomogram were used to estimate the progression-free survival (PFS) and overall survival (OS) in the validation dataset. Results: Both single sequence–based Radscores and mpMRI radiomics nomogram were associated with pathologic response (p < 0.001). The nomogram achieved the highest diagnostic ability with area under ROC curve of 0.844 (95% CI, 0.749–0.914) and 0.820 (95% CI, 0.695–0.910) in the training and validation datasets. The hazard ratio of the nomogram for PFS and OS prediction was 2.597 (95% CI: 1.046–6.451, log-rank p = 0.023) and 2.570 (95% CI: 1.166–5.666, log-rank p = 0.011). Conclusions: The mpMRI-based radiomics nomogram showed preferable performance in predicting pathologic response to NAC in LAGC. Key Points: • This study investigated the value of multi-parametric MRI-based radiomics in predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. • The nomogram incorporating T2WI Radscore, ADC Radscore, and DCE Radscore as well as Borrmann classification outperformed the single sequence–based Radscore. • The nomogram also exhibited a promising prognostic ability for patient survival and enriched radiomics studies in gastric cancer. [ABSTRACT FROM AUTHOR]

Objectives: This prospective study compared the detection efficacy of analog 18F-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) (aF PET/CT), digital [18F]FDG PET/CT (dF PET/CT), and digital 13N-ammonia (13N-NH3) PET/CT (dN PET/CT) for patients with lung adenocarcinoma featuring ground glass nodules (GGNs). Methods: Eighty-seven patients with lung adenocarcinoma featuring GGNs who underwent dF and dN PET/CT were enrolled. Based on the GGN component, diameter, and solid-part size, 87 corresponding patients examined using aF PET/CT were included, with age, sex, and lesion characteristics closely matched. Images were visually evaluated, and the tumor to background ratio (TBR) was used for semi-quantitative analysis. Results: Ultimately, 40 and 47 patients with pure GGNs (pGGNs) and mixed GGNs (mGGNs), respectively, were included. dF PET/CT revealed more positive lesions and higher tracer uptake in GGNs than did aF PET/CT (53/87 vs. 26/87, p < 0.05; TBR: 3.08 ± 4.85 vs. 1.42 ± 0.93, p < 0.05), especially in mGGNs (44/47 vs. 26/47, p < 0.05; TBR: 4.48 ± 6.17 vs. 1.78 ± 1.16, p < 0.05). However, dN PET/CT detected more positive lesions than did dF PET/CT (71/87 vs. 53/87, p < 0.05), especially in pGGNs (24/40 vs. 9/40, p < 0.05). Conclusions: dF PET/CT provides superior detection efficacy over aF PET/CT for patients with lung adenocarcinoma featuring GGNs, particularly mGGNs. dN PET/CT revealed superior detection efficacy over dF PET/CT, particularly in pGGNs. aF, dF, and dN PET/CT are valuable non-invasive examinations for lung cancer featuring GGNs, with dN PET/CT offering the best detection performance. Key Points: • Digital PET/CT provides superior detection efficacy over analog PET/CT in patients with lung adenocarcinoma featuring GGNs. • dN PET/CT can offer more help in the early detection of malignant GGN. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma. Methods: A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients' clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test. Results: The proposed DL signature yielded an AUC of 0.948–0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature. Conclusions: The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options. Key Points: • Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma. • The deep learning signature yielded an AUC of 0.948–0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model. • The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature. [ABSTRACT FROM AUTHOR]

This is especially important when considering that, with actual NCCN criteria, about 37% of patients with a PDAC deemed resectable on CT will present margin-positive resection at surgery (R1 or R2) [[9]]. Estimating Recurrence after Upfront Surgery in Patients with Resectable Pancreatic Ductal Adenocarcinoma by Using Pancreatic CT: Development and Validation of a Risk Score. [Extracted from the article]

Objectives: To explore the diagnostic performance of EFSUMB CEUS Pancreatic Applications guidelines (version 2017) before and after the addition of iso-enhancement and very fast/fast washout as supplementary diagnostic criteria for PDAC. Methods: In this retrospective study, patients diagnosed with solid pancreatic lesions from January 2017 to December 2020 were evaluated. Pancreatic ductal adenocarcinoma (PDAC) is reported to show hypo-enhancement in all phases according to the EFSUMB guidelines. First, based on this definition, all lesions were categorized as PDAC and non-PDAC. Then, iso-enhancement and very fast/fast washout were added as supplementary diagnostic criteria, and all lesions were recategorized. The diagnostic performance was assessed in terms of the accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV). The reference standard consisted of histologic evaluation or composite imaging and clinical follow-up findings. Results: A total of 455 nodules in 450 patients (median age, 58.37 years; 250 men) were included. The diagnostic performance using the EFSUMB CEUS guidelines for PDAC had an ACC of 69.5%, SEN of 65.4%, SPE of 84%, PPV of 93.5%, NPV of 40.6%, and ROC of 0.747. After recategorization according to the supplementary diagnostic criteria, the diagnostic performance for PDAC had an ACC of 95.8%, SEN of 99.2%, SPE of 84%, PPV of 95.7%, NPV of 96.6%, and ROC of 0.916. Conclusion: The EFSUMB guidelines and recommendations for pancreatic lesions can effectively identify PDAC via hypo-enhancement on CEUS. However, the diagnostic performance may be further improved by the reclassification of PDAC lesions after adding iso-enhancement and very fast/fast washout mode. Key Points: • In the EFSUMB guidelines, the only diagnostic criterion for PDAC is hypo-enhancement, to which iso-enhancement and very fast/fast washout mode were added in our research. • Using hypo-enhancement/iso-enhancement with very fast/fast washout patterns as the diagnostic criteria for PDAC for solid pancreatic masses on CEUS has high diagnostic accuracy. • The blood supply pattern of PDAC can provide important information, and CEUS has unique advantages in this respect due to its real-time dynamic attenuation ability. [ABSTRACT FROM AUTHOR]

Objectives: To investigate thin-section computed tomography (CT) features of pulmonary subsolid nodules (SSNs) with sizes between 5 and 20 mm to determine predictive factors for differentiating focal interstitial fibrosis (FIF) from adenocarcinoma. Methods: From January 2017 to December 2018, 169 patients who had persistent SSNs 5–20 mm in size and underwent preoperative nodule localization were enrolled. Patient characteristics and thin-section CT features of the SSNs were reviewed and compared between the FIF and adenocarcinoma groups. Univariable and multivariable analyses were used to identify predictive factors of malignancy. Receiver operating characteristic (ROC) curve analysis was used to quantify the performance of these factors. Results: Among the 169 enrolled SSNs, 103 nodules (60.9%) presented as pure ground-glass opacities (GGOs), and 40 (23.7%) were FIFs. Between the FIF and adenocarcinoma groups, there were significant differences (p< 0.05) in nodule border, shape, thickness, and coronal/axial (C/A) ratio. Multivariable analysis demonstrated that a well-defined border, a nodule thickness >4.2, and a C/A ratio >0.62 were significant independent predictors of malignancy. The performance of a model that incorporated these three predictors in discriminating FIF from adenocarcinoma achieved a high area under the ROC curve (AUC, 0.979) and specificity (97.5%). Conclusions: For evaluating persistent SSNs 5–20 mm in size, the combination of a well-defined border, a nodule thickness > 4.2, and a C/A ratio > 0.62 is strongly correlated with malignancy. High accuracy and specificity can be achieved by using this predictive model. Key Points: • Thin-section coronal images play an important role in differentiating FIF from adenocarcinoma. • The combination of a well-defined border, nodule thickness>4.2 mm, and C/A ratio >0.62 is associated with malignancy. • This predictive model may be helpful for managing persistent SSNs between 5 and 20 mm in size. [ABSTRACT FROM AUTHOR]

Objectives: For patients with pancreatic adenocarcinoma (PAC), adequate determination of disease extent is critical for optimal management. We aimed to evaluate diagnostic accuracy of CT in determining the resectability of PAC based on 2020 NCCN Guidelines. Methods: We retrospectively enrolled 368 consecutive patients who underwent upfront surgery for PAC and preoperative pancreas protocol CT from January 2012 to December 2017. The resectability of PAC was assessed based on 2020 NCCN Guidelines and compared to 2017 NCCN Guidelines using chi-square tests. Overall survival (OS) was estimated using the Kaplan-Meier method and compared using log-rank test. R0 resection–associated factors were identified using logistic regression analysis. Results: R0 rates were 80.8% (189/234), 67% (71/106), and 10.7% (3/28) for resectable, borderline resectable, and unresectable PAC according to 2020 NCCN Guidelines, respectively (p < 0.001). The estimated 3-year OS was 28.9% for borderline resectable PAC, which was significantly lower than for resectable PAC (43.6%) (p = 0.004) but significantly higher than for unresectable PAC (0.0%) (p < 0.001). R0 rate was significantly lower in patients with unresectable PAC according to 2020 NCCN Guidelines (10.7%, 3/28) than in those with unresectable PAC according to the previous version (31.7%, 20/63) (p = 0.038). In resectable PAC, tumor size ≥ 3 cm (p = 0.03) and abutment to portal vein (PV) (p = 0.04) were independently associated with margin-positive resection. Conclusions: The current NCCN Guidelines are useful for stratifying patients according to prognosis and perform better in R0 prediction in unresectable PAC than the previous version. Larger tumor size and abutment to PV were associated with margin-positive resection in patients with resectable PAC. Key Points: • The updated 2020 NCCN Guidelines were useful for stratifying patients according to prognosis. • The updated 2020 NCCN Guidelines performed better in the prediction of margin-positive resection in unresectable cases than the previous version. • Tumor size ≥ 3 cm and abutment to the portal vein were associated with margin-positive resection in patients with resectable pancreatic adenocarcinoma. [ABSTRACT FROM AUTHOR]

Objectives: To summarise the CT findings of gastric poorly cohesive carcinoma (PCC) in the 40 s late arterial phase and differentiate it from tubular adenocarcinoma (TAC) using an integrative nomogram. Methods: A total of 241 patients including 59 PCCs, 109 TACs, and 73 other type gastric cancers were enrolled. Thirteen CT morphological characteristics of each lesion in the late arterial phase were evaluated. In addition, CT value–related parameters were extracted from ROIs encompassing the area of greatest enhancement on four-phase CT images. Nomograms based on regression models were built to discriminate PCCs from TACs and from non-PCCs. ROC curve analysis was performed to assess the diagnostic efficiency. Results: Six morphological characteristics, 10 CT value–related parameters, and the enhanced curve types differed significantly among the above three groups in the primary cohort (all p < 0.05). The paired comparison revealed that 10 CT value–related parameters differed significantly between PCCs and TACs (all p < 0.05). The AUC of the nomogram based on the multivariate model for discriminating PCCs from TACs was 0.954, which was confirmed in the validation cohort (AUC = 0.895). The AUC of another nomogram for discriminating PCCs from non-PCCs was 0.938, which was confirmed in the validation cohort (AUC = 0.880). Conclusions: In the 40 s late arterial phase, the morphological characteristics and CT value–related parameters were significantly different among PCCs, TACs, and other types. PCCs were prone to manifest mucosal line interruption, diffuse thickening, infiltrative growth, and slow-rising enhanced curve (Type A). Furthermore, multivariate models were useful in discriminating PCCs from TACs and other types. Key Points: • Multiple morphological characteristics and CT value–related parameters differed significantly between gastric PCCs and TACs in the 40 s late arterial phase. • The nomogram integrating morphological characteristics and CT value–related parameters in the 40 s late arterial phase had favourable performance in discriminating PCCs from TACs. • More useful information can be derived from 40 s late arterial phase CT images; thus, a more accurate evaluation can be made in clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of a fractional order calculus (FROC) diffusion model for imaging-based assessment of Lauren classification in gastric adenocarcinoma. Methods: In this study, 43 patients (15 females, 28 males) with gastric adenocarcinoma underwent MRI at 1.5 T. According to pathology-based Lauren classification, 10 patients had diffuse-type, 20 had intestinal-type, and 13 had mixed-type lesions. The diffuse and mixed types were combined as diffuse-and-mixed type to be differentiated from the intestinal type using diffusion MRI. Diffusion-weighted images were acquired by using eleven b-values (0–2000 s/mm2). Three FROC model parameters comprising diffusion coefficient D, intravoxel diffusion heterogeneity β, and a microstructural quantity μ, together with a conventional apparent diffusion coefficient (ADC), were estimated. The mean parameter values in the tumour were computed by using a percentile histogram analysis. Individual or linear combinations of the mean parameters in the tumour were used to differentiate the diffuse-and-mixed type from the intestinal type using descriptive statistics and receiver operating characteristic (ROC) analyses. Results: Significant differences were observed between diffuse-and-mixed-type and intestinal-type lesions in D (0.99 ± 0.20 μm2/ms vs. 1.11 ± 0.23 μm2/ms; p = 0.036), β (0.37 ± 0.08 vs. 0.43 ± 0.11; p = 0.043), μ (7.92 ± 2.79 μm vs. 9.87 ± 1.52 μm; p = 0.038), and ADC (0.81 ± 0.34 μm2/ms vs. 0.96 ± 0.19 μm2/ms; p = 0.033). Among the individual parameters, μ produced the largest area under the ROC curve (0.739). The combinations of (D, β, μ) and (β and μ) produced the best overall performance with a sensitivity of 0.739, specificity of 0.750, accuracy of 0.744, and area under the curve of 0.793 (95% confidence interval: 0.657–0.929). Conclusion: Diffusion MRI with the FROC model holds promise for non-invasive assessment of Lauren classification for gastric adenocarcinoma. Key Points: • High b-value diffusion MRI with a FROC model that is sensitive to tissue microstructures can differentiate the diffuse-and-mixed type from intestinal type of gastric adenocarcinoma. • The combination of FROC parameters produced the best result for distinguishing the diffuse-and-mixed type from the intestinal type with an area under the receiver operating characteristic curve of 0.793. • The FROC model parameters, individually or conjointly, hold promise for repeated, non-invasive evaluations of gastric adenocarcinoma at various time points throughout disease progression or regression to complement conventional Lauren classification. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to systematically evaluate the diagnostic accuracy of CT-determined resectability following neoadjuvant treatment for predicting margin-negative resection (R0 resection) in patients with pancreatic ductal adenocarcinoma (PDAC).Methods: Original studies with sufficient details to obtain the sensitivity and specificity of CT-determined resectability following neoadjuvant treatment, with a reference on the pathological margin status, were identified in PubMed, EMBASE, and Cochrane databases until February 24, 2020. The identified studies were divided into two groups based on the criteria of R0 resectable tumor (ordinary criterion: resectable PDAC alone; extended criterion: resectable and borderline resectable PDAC). The meta-analytic summary of the sensitivity and specificity for each criterion was estimated separately using a bivariate random-effect model. Summary results of the two criteria were compared using a joint-model bivariate meta-regression.Results: Of 739 studies initially searched, 6 studies (6 with ordinary criterion and 5 with extended criterion) were included for analysis. The meta-analytic summary of sensitivity and specificity was 45% (95% confidence interval [CI], 19-73%; I2 = 88.3%) and 85% (95% CI, 65-94%; I2 = 60.5%) for the ordinary criterion, and 81% (95% CI, 71-87%; I2 = 0.0%) and 42% (95% CI, 28-57%; I2 = 6.2%) for the extended criterion, respectively. The diagnostic accuracy significantly differed between the two criteria (p = 0.02).Conclusions: For determining resectability on CT, the ordinary criterion might be highly specific but insensitive for predicting R0 resection, whereas the extended criterion increased sensitivity but would decrease specificity. Further investigations using quantitative parameters may improve the identification of R0 resection.Key Points: • CT-determined resectability of PDAC after neoadjuvant treatment using the ordinary criterion shows low sensitivity and high specificity in predicting R0 resection. • With the extended criterion, CT-determined resectability shows higher sensitivity but lower specificity than with the ordinary criterion. • CT-determined resectability with both criteria achieved suboptimal diagnostic performances, suggesting that care should be taken while selecting surgical candidates and when determining the surgical extent after neoadjuvant treatment in patients with PDAC. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the utility of 3D amide proton transfer (APT) MRI in predicting pathologic factors for rectal adenocarcinoma, in comparison with diffusion kurtosis imaging.Methods: Sixty-one patients with rectal adenocarcinoma were enrolled in this prospective study. 3D APT and diffusion kurtosis imaging (DKI) were performed. Mean APT-weighted signal intensity (APTw SI), mean kurtosis (MK), mean diffusivity (MD), and ADC values of tumors were calculated on these maps. Pathological analysis included WHO grades, pT stages, pN stages, and extramural venous invasion (EMVI) status. Student's t test, Spearman correlation, and receiver operating characteristics (ROC) analysis were used for statistical analysis.Results: High-grade rectal adenocarcinoma showed significantly higher mean APTw SI and MK values (2.771 ± 0.384 vs 2.108 ± 0.409, 1.167 ± 0.216 vs 1.045 ± 0.175, respectively; p < 0.05). T3 rectal adenocarcinoma demonstrated higher mean APTw SI and MK than T2 tumors (2.433 ± 0.467 vs 1.900 ± 0.302, p < 0.05). No kurtosis, diffusivity, and ADC differences were found between T2 and T3 tumors. Tumors with lymph node metastasis and EMVI involvement showed significantly higher mean APTw SI, MK. No difference was found in diffusivity and ADC between pN0 and pN1-2 groups, and EMVI-negative and EMVI-positive statuses. Mean APTw SI exhibited a significantly high positive correlation with WHO grades, demonstrating 92.31% sensitivity and 79.17% specificity for distinguishing low- from high-grade rectal adenocarcinoma, providing a better diagnostic capacity than MK, MD, and mean ADC values.Conclusion: 3D-APT could serve as a non-invasive biomarker for evaluating prognostic factors of rectal adenocarcinoma.Key Points: • Mean APTw SI was significantly higher in high-grade compared to low-grade rectal adenocarcinoma. • Mean APTw SI was significantly higher in T3 stage rectal adenocarcinoma, with lymph node metastasis, or in EMVI-positive status. • APTw SI exhibited greater diagnostic capability in discriminating low-grade from high-grade rectal adenocarcinoma, compared with kurtosis, diffusivity, and ADC. [ABSTRACT FROM AUTHOR]

Objectives: To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN). Methods: Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared. Results: Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01). Conclusions: The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances. Key Points: • The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model. [ABSTRACT FROM AUTHOR]

Objectives: To predict poor survival and tumor recurrence in patients with ampullary adenocarcinoma using preoperative clinical and CT findings. Materials and methods: A total of 216 patients with ampullary adenocarcinoma who underwent preoperative CT and surgery were retrospectively included. CT was assessed by two radiologists. Clinical and histopathological characteristics including histologic subtypes were investigated. A Cox proportional hazard model and the Kaplan-Meier method were used to identify disease-free survival (DFS) and overall survival (OS). A nomogram was created based on the multivariate analysis. The optimal cutoff size of the tumor was evaluated and validated by internal cross validation. Results: The median OS was 62.8 ± 37.9, and the median DFS was 54.3 ± 41.2 months. For OS, tumor size (hazard ratio [HR] 2.79, p < 0.001), papillary bulging (HR 0.63, p = 0.049), organ invasion on CT (HR 1.92, p = 0.04), male sex (HR 1.59, p = 0.046), elevated CA 19–9 (HR 1.92, p = 0.01), pT stage (HR 2.45, p = 0.001), and pN stage (HR 3.04, p < 0.001) were important predictors of survival. In terms of recurrence, tumor size (HR 2.37, p = 0.04), pT stage (HR 1.76, p = 0.03), pN stage (HR 2.23, p = 0.001), and histologic differentiation (HR 4.31, p = 0.008) were important predictors of recurrence. In terms of tumor size on CT, 2.65 cm and 3.15 cm were significant cutoff values for poor OS and RFS (p < 0.001). Conclusion: Preoperative clinical and CT findings were useful to predict the outcomes of ampullary adenocarcinoma. In particular, tumor size, papillary bulging, organ invasion on CT, male sex, and elevated CA 19–9 were important predictors of poor survival after surgery. Key Points: • Clinical staging based on preoperative clinical information and CT findings can be useful to predict the prognosis of ampullary adenocarcinoma patients. • In terms of survival, tumor size (HR 2.79), papillary bulging (HR 0.63), organ invasion on CT (HR 1.92), male sex (HR 1.59), and elevated CA 19–9 (HR 1.92) were important clinical predictors of poor survival. • Tumor size on CT was of special importance for both poor overall survival and disease-free survival, with optimal cutoff values of 2.65 cm and 3.15 cm, respectively (p < 0.001). [ABSTRACT FROM AUTHOR]

Objectives: To retrospectively examine US, CT, and MR imaging examinations of missed or misinterpreted pancreatic ductal adenocarcinoma (PDAC), and identify factors which may have confounded detection or interpretation. Methods: We reviewed 107 examinations in 66/257 patients (26%, mean age 73.7 years) diagnosed with PDAC in 2014 and 2015, with missed or misinterpreted imaging findings as determined by a prior study. For each patient, images and reports were independently reviewed by two radiologists, and in consensus, the following factors which may have confounded assessment were recorded: inherent tumor factors, concurrent pancreatic pathology, technical limitations, and cognitive biases. Secondary signs of PDAC associated with each examination were recorded and compared with the original report to determine which findings were missed. Results: There were 66/107 (62%) and 49/107 (46%) cases with missed and misinterpreted imaging findings, respectively. A significant number of missed tumors were < 2 cm (45/107, 42%), isoattenuating on CT (32/72, 44%) or non-contour deforming (44/107, 41%). Most (29/49, 59%) misinterpreted examinations were reported as uncomplicated pancreatitis. Almost all examinations (94/107, 88%) demonstrated secondary signs; pancreatic duct dilation was the most common (65/107, 61%) and vascular invasion was the most commonly missed 35/39 (90%). Of the CT and MRIs, 28 of 88 (32%) had suboptimal contrast dosing. Inattentional blindness was the most common cognitive bias, identified in 55/107 (51%) of the exams. Conclusion: Recognizing pitfalls of PDAC detection and interpretation, including intrinsic tumor features, secondary signs, technical factors, and cognitive biases, can assist radiologists in making an early and accurate diagnosis. Key Points: • There were 66/107 (62%) and 49/107 (46%) cases with missed and misinterpreted imaging findings, respectively, with tumoral, technical, and cognitive factors leading to the misdiagnosis of pancreatic ductal adenocarcinoma. • The majority (29/49, 59%) of misinterpreted cases of pancreatic ductal adenocarcinoma were mistaken for pancreatitis, where an underlying mass or secondary signs were not appreciated due to inflammatory changes. • The most common missed secondary sign of pancreatic ductal adenocarcinoma was vascular encasement, missed in 35/39 (90%) of cases, indicating the importance of evaluating the peri-pancreatic vasculature. [ABSTRACT FROM AUTHOR]

Objectives: To perform a correlation analysis between histopathology and imaging in patients with previously untreated pancreatic ductal adenocarcinoma (PDAC) and to determine the prognostic values of clinical, histological, and imaging parameters regarding overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS). Methods: This single-centre study prospectively included 61 patients (32 males; median age, 68.0 years [IQR, 63.0–75.0 years]) with histologically confirmed PDAC and following surgical resection who preoperatively underwent 18F-FDG PET/CT and DW-MRI. On whole lesions, we measured, using a 42% SUVmax threshold volume of interest (VOI), the following quantitative parameters: mean and maximum standardised uptake values (SUVmean and SUVmax), total lesion glycolysis (TLG), metabolic tumour volume (MTV), mean and minimum apparent diffusion coefficient (ADCmean and ADCmin), diffusion total volume (DTV), and MTV/ADCmin ratio. Spearman's correlation analysis was performed to assess relationships between these markers and histopathological findings from surgical specimens (stage; grade; resection quality; and vascular, perineural, and lymphatic invasion). Kaplan-Meier and Cox hazard ratio methods were used to evaluate the impacts of imaging parameters on OS (n = 41), DSS (n = 36), and PFS (n = 41). Results: Inverse correlations between ADCmin and SUVmax (rho = − 0.34; p = 0.0071), and between SUVmean and ADCmean (rho = − 0.29; p = 0.026) were identified. ADCmin was inversely correlated with tumour grade (rho = − 0.40; p = 0.0015). MTV was an independent predictive factor for OS and DSS, while DTV was an independent predictive factor for PFS. Conclusion: In previously untreated PDAC, ADC and SUV values are correlated. Combining PET-MRI metrics may help predict PDAC grade and patients' survival. Key Points: • Minimum apparent diffusion coefficient derived from DW-MRI inversely correlates with tumour grade in pancreatic ductal adenocarcinoma. • In pancreatic ductal adenocarcinoma, metabolic tumour volume has been confirmed as a predictive factor for patients' overall survival and disease-specific survival. • Combining PET and MRI metrics may help predict grade and patients' survival in pancreatic ductal adenocarcinoma. [ABSTRACT FROM AUTHOR]

Objectives: To assess the proportion of missed/misinterpreted imaging examinations of pancreatic ductal adenocarcinoma (PDAC), and their association with the diagnostic interval and survival.Methods: Two hundred fifty-seven patients (mean age, 71.8 years) diagnosed with PDAC in 2014-2015 were identified from the Nova Scotia Cancer Registry. Demographics, stage, tumor location, and dates of initial presentation, diagnosis, and, if applicable, surgery and death were recorded. US, CT, and MRI examinations during the diagnostic interval were independently graded by two radiologists using the RADPEER system; discordance was resolved in consensus. Mean diagnostic interval and survival were compared amongst RADPEER groups (one-way ANOVA). Kaplan-Meier analysis was performed for age (< 65, 65-79, ≥ 80), sex, tumor location (proximal/distal), stage (I-IV), surgery (yes/no), chemotherapy (yes/no), and RADPEER score (1-3). Association between these covariates and survival was assessed (multivariate Cox proportion hazards model).Results: RADPEER 1-3 scores were assigned to 191, 27, and 39 patients, respectively. Mean diagnostic intervals were 53, 86, and 192 days, respectively (p = 0.018). There were only 3/257 (1.2%) survivors. Mean survival was not different between groups (p = 0.43). Kaplan-Meier analysis showed worse survival in RADPEER 1-2 (p = 0.007), older age (p < 0.001), distal PDAC (p = 0.016), stage (p < 0.0001), and no surgery (p < 0.001); survival was not different with sex (p = 0.083). Cox analysis showed better survival in RADPEER 3 (p = 0.005), women (p = 0.002), surgical patients (p < 0.001), and chemotherapy (p < 0.001), and worse survival in stage IV (p = 0.006).Conclusion: Imaging-related delays occurred in one-fourth of patients and were associated with longer diagnostic intervals but not worse survival, potentially due to overall poor survival in the cohort.Key Points: • One-fourth of patients (66/257, 25.7%) with pancreatic ductal adenocarcinoma (PDAC) underwent imaging examinations that demonstrated manifestations of the disease, but findings were either missed or misinterpreted; RADPEER 2 and 3 scores were assigned to 10.5% and 15.2% of patients, respectively. • Patients with imaging examinations assigned RADPEER 3 scores were associated with significantly longer diagnostic intervals (192 ± 323 days) than RADPEER 1 (53 ± 86 days) and RADPEER 2 (86 ± 120 days) (p < 0.001). • Imaging-related diagnostic delays were not associated with worse survival; however, this may have been confounded by the overall poor survival in our cohort (only 3/257 (1.2%) survivors). [ABSTRACT FROM AUTHOR]

Objectives: To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). Methods: Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. Results: Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. Conclusions: Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. Key Points: • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating cervical cancer (CC) pathological type, grade, and stage.Methods: One hundred and twelve women underwent pelvic APTWI and DKI. The magnetization transfer ratio asymmetry (MTRasym, 3.5 ppm), apparent kurtosis coefficient (Kapp), and non-Gaussian diffusion coefficient (Dapp) were calculated by histological subtype, grade, and stage. The differences, efficacy, and correlation between parameters were determined.Results: The MTRasym(3.5 ppm) and Dapp values of the adenocarcinoma (CA) group were higher than those of the cervical squamous carcinoma (CSC) group, while the Kapp values were lower than those of the CSC group. The MTRasym(3.5 ppm) and Kapp values of the high-grade group were higher than those of the low-grade group, while the Dapp values were lower than those of the low-grade group. The Dapp values of the advanced-stage group were lower than those of the early-stage group, while the Kapp values were greater than those of the early-stage group. The Kapp showed the highest efficacy in differentiating CSC and CA, high- and low-grade CC, and advanced- and early-stage CC. In the CSC and CA groups, both the Kapp and Dapp were highly correlated with pathological grade, and the MTRasym(3.5 ppm) was weakly correlated with pathological grade. The Kapp, Dapp, and MTRasym(3.5 ppm) were all weakly correlated with pathological stage.Conclusion: Both DKI and APTWI can be used in preliminary evaluations of CC, but DKI has advantages in the identification of pathological type, grade, and stage.Key Points: • PTWI and DKI provide new information regarding cervical cancer. • MTRasym(3.5 ppm), Dapp, and Kapp are valid parameters to characterize tissue microstructure. • DKI is superior to APTWI in the study of cervical cancer. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the diagnostic performance of dynamic contrast-enhanced (DCE)-MRI in predicting malignancy after percutaneous biopsy diagnosis of atypical ductal hyperplasia (ADH).Methods and Materials: In this retrospective study, 68 lesions (66 women) with percutaneous biopsy diagnosis of ADH and pre-operative breast DCE-MRI performed between January 2016 and December 2017 were included. Two radiologists reviewed in consensus mammography, ultrasound, and MR images. The final diagnosis after surgical excision was used as standard of reference. Clinical and imaging features were compared in patients with and without upgrade to malignancy after surgery. The diagnostic performance of DCE-MRI in predicting malignant upgrade was evaluated.Results: A 9-gauge vacuum-assisted biopsy was performed in 40 (58.8%) cases and a 14-gauge core needle biopsy in 28 (41.2%) cases. Upgrade to malignancy was observed in 17/68 (25%) lesions, including 4/17 (23.5%) cases of invasive cancer and 13/17 (76.5%) cases of ductal carcinoma in situ (DCIS). In 16/17 (94.1%) malignant and 20/51 (39.2%) benign lesions, a suspicious enhancement could be recognized in DCE-MRI. The malignant lesion without suspicious enhancement was a low-grade DCIS (4 mm size). Sensitivity, specificity, positive predictive value, and negative predictive value of DCE-MRI on predicting malignancy were respectively 94.1%, 60.7%, 44.4%, and 96.8%. No other clinical or imaging features were significantly different in patients with and without upgrade to malignancy.Conclusion: After a percutaneous biopsy diagnosis of ADH, malignancy can be ruled out in most of the cases, if no suspicious enhancement is present in the biopsy area at DCE-MRI. Breast DCE-MRI may be used to avoid surgery in more than half of the patients with final benign diagnosis.Key Points: • Breast DCE-MRI can safely rule out malignancy if no suspicious enhancement is present in the biopsy area after a percutaneous biopsy diagnosis of ADH. • All cases of upgrade to high-grade DCIS and invasive cancers can be identified at breast DCE-MRI after a percutaneous biopsy diagnosis of ADH. • Breast DCE-MRI may be used to avoid surgery in more than half of the patients with final benign diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography (CT)‑based radiomics model for preoperative prediction of STAS in lung adenocarcinoma.Methods and Materials: This retrospective study was approved by an institutional review board and included 462 (mean age, 58.06 years) patients with pathologically confirmed lung adenocarcinoma. STAS was identified in 90 patients (19.5%). Two experienced radiologists segmented and extracted radiomics features on preoperative thin-slice CT images with radiomics extension independently. Intraclass correlation coefficients (ICC) and Pearson's correlation were used to rule out those low reliable (ICC < 0.75) and redundant (r > 0.9) features. Univariate logistic regression was applied to select radiomics features which were associated with STAS. A radiomics-based machine learning predictive model using a random forest (RF) was developed and calibrated with fivefold cross-validation. The diagnostic performance of the model was measured by the area under the curve (AUC) of receiver operating characteristic (ROC).Results: With univariate analysis, 12 radiomics features and age were found to be associated with STAS significantly. The RF model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.Conclusion: CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance.Key Points: • CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy. • The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS. [ABSTRACT FROM AUTHOR]

Purpose: To assess the additional value of quantitative tCho evaluation to diagnose malignancy and lymph node metastases in suspicious lesions on multiparametric breast MRI (mpMRI, BI-RADS 4, and BI-RADS 5).Methods: One hundred twenty-one patients that demonstrated suspicious multiparametric breast MRI lesions using DCE, T2w, and diffusion-weighted (DW) images were prospectively enrolled in this IRB-approved study. All underwent single-voxel proton MR spectroscopy (1H-MRS, point-resolved spectroscopy sequence, TR 2000 ms, TE 272 ms) with and without water suppression. The total choline (tCho) amplitude was measured and normalized to millimoles/liter according to established methodology by two independent readers (R1, R2). ROC-analysis was employed to predict malignancy and lymph node status by tCho results.Results: One hundred three patients with 74 malignant and 29 benign lesions had full 1H-MRS data. The area under the ROC curve (AUC) for prediction of malignancy was 0.816 (R1) and 0.809 (R2). A cutoff of 0.8 mmol/l tCho could diagnose malignancy with a sensitivity of > 95%. For prediction of lymph node metastases, tCho measurements achieved an AUC of 0.760 (R1) and 0.788 (R2). At tCho levels < 2.4 mmol/l, no metastatic lymph nodes were found.Conclusion: Quantitative tCho evaluation from 1H-MRS allowed diagnose malignancy and lymph node status in breast lesions suspicious on multiparametric breast MRI. tCho therefore demonstrated the potential to downgrade suspicious mpMRI lesions and stratify the risk of lymph node metastases for improved patient management.Key Points: • Quantitative tCho evaluation can distinguish benign from malignant breast lesions suspicious after multiparametric MRI assessment. • Quantitative tCho levels are associated with lymph node status in breast cancer. • Quantitative tCho levels are higher in hormonal receptor positive compared to hormonal receptor negative lesions. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model and expert radiologists.Methods: This study included 525 patients (309 women; median, 62 years) to develop models, and an independent cohort of 101 patients (57 women; median, 66 years) was used for validation. A size-based logistic model and deep learning models using 2.5-dimension (2.5D) and three-dimension (3D) CT images were developed to discriminate IAC from less invasive pathologies. Overall performance, discrimination, and calibration were assessed. Diagnostic performances of the three thoracic radiologists were compared with those of the deep learning model.Results: The overall performances of the deep learning models (Brier score, 0.122 for the 2.5D DenseNet and 0.121 for the 3D DenseNet) were superior to those of the size-based logistic model (Brier score, 0.198). The area under the receiver operating characteristic curve (AUC) of the 2.5D DenseNet (0.921) was significantly higher than that of the 3D DenseNet (0.835; p = 0.037) and the size-based logistic model (0.836; p = 0.009). At equally high sensitivities of 90%, the 2.5D DenseNet showed significantly higher specificity (88.2%; all p < 0.05) and positive predictive value (97.4%; all p < 0.05) than other models. Model calibration was poor for all models (all p < 0.05). The 2.5D DenseNet had a comparable performance with the radiologists (AUC, 0.848-0.910).Conclusion: The 2.5D DenseNet model could be used as a highly sensitive and specific diagnostic tool to differentiate IACs among SSNs for surgical candidates.Key Points: • The deep learning model developed using 2.5D DenseNet showed higher overall performance and discrimination than the size-based logistic model for the differentiation of invasive adenocarcinomas among subsolid nodules for surgical candidates. • The 2.5D DenseNet demonstrated a thoracic radiologist-level diagnostic performance and had higher specificity (88.2%) at equal sensitivities (90%) than the size-based logistic model (specificity, 52.9%). • The 2.5D DenseNet could be used to reduce potential overtreatment for the indolent subsolid nodules or to select candidates for sublobar resection instead of the standard lobectomy. [ABSTRACT FROM AUTHOR]

Objective: The purpose of the study was to investigate the role of CT radiomics for the preoperative distinction of intestinal-type gastric adenocarcinomas.Materials and Methods: A total of 187 consecutive patients with preoperative contrast CT examination and pathologically proven gastric adenocarcinoma were retrospectively collected. Patients were divided into a training set (n = 150) and a test set (n = 37). Arterial phase (AP), portal phase (PP), and delay phase (DP) images were retrieved for analysis. A dedicated postprocessing software was used to segment the lesions and extract radiomics features. Random forest (RF) algorithm was applied to construct the classifier models. A nomogram was developed by incorporating multiphase radiomics scores. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the radiomics model and nomogram in both sets.Results: The radiomics model showed a favorable capability in the distinction of intestinal-type gastric adenocarcinomas. The areas under curves (AUCs) of the AP, PP, and DP radiomics models were 0.754 (95% CI: 0.676, 0.820), 0.815 (95% CI: 0.744, 0.874), and 0.764 (95% CI: 0.688, 0.829) in the training set, respectively, which were confirmed in the test set with AUCs of 0.742 (95% CI: 0.572, 0.872), 0.775 (95% CI: 0.608, 0.895), and 0.857 (95% CI: 0.703, 0.950), respectively. The nomogram yielded excellent performance for distinguishing intestinal-type adenocarcinomas in both sets, with AUCs of 0.928 (95%: 0.875, 0.964) and 0.904 (95% CI: 0.761, 0.976).Conclusions: The multiphase CT radiomics nomogram holds promise for the individual preoperative discrimination of intestinal-type gastric adenocarcinoma.Key Points: • CT radiomics has a potential role in the distinction of intestinal-type gastric adenocarcinomas. • Single-phase enhanced CT-based radiomics showed favorable capability in distinguishing intestinal-type tumors. • The nomogram which incorporates the multiphase radiomics scores could facilitate the individual prediction of intestinal-type lesions. [ABSTRACT FROM AUTHOR]

Objectives: To identify MRI features that are helpful for the differentiation of gallbladder neuroendocrine tumors (GB-NETs) from gallbladder adenocarcinomas (GB-ADCs) and to evaluate their prognostic values.Methods: Between January 2008 and December 2018, we retrospectively enrolled patients who underwent MRI for GB malignancy. Two radiologists independently assessed the MRI findings and reached a consensus. Significant MRI features, which distinguish GB-NETs from GB-ADCs, were identified. Cox regression analyses were performed to find MRI features that were prognostic for overall survival.Results: There were 63 patients with GB-NETs (n = 21) and GB-ADCs (n = 42). Compared with GB-ADCs, GB-NETs more frequently demonstrated the following MRI features: well-defined margins, intact overlying mucosa, and thick rim contrast enhancement and/or diffusion restriction (ps < 0.001). Liver metastases were more common and demonstrated thick rim contrast enhancement and diffusion restriction in GB-NETs (ps < 0.001). Lymph node (LN) metastasis showed thick rim diffusion restriction more often in GB-NETs than in GB-ADCs (p = 0.009). On quantitative analysis, the sizes of the GB mass and metastatic LNs in GB-NETs were larger than those in GB-ADCs (p = 0.002 and p = 0.010, respectively). The ratio of apparent diffusion coefficient values between the lesion and the spleen was lower in the GB mass, liver metastases, and LN metastases of GB-NETs than those of GB-ADCs (p < 0.001, p = 0.017, and p < 0.001, respectively). Survival analysis revealed that a large metastatic LN (hazard ratio 1.737; 95% confidence interval, 1.112-2.712) was the only poor prognostic factor (p = 0.015).Conclusion: Several MRI features aided in differentiating between GB-NETs and GB-ADCs. A large metastatic LN was associated with poor survival.Key Points: • Compared with gallbladder adenocarcinomas (GB-ADCs), neuroendocrine tumors (GB-NETs) and their metastases to the liver and lymph nodes more frequently demonstrated a thick rim appearance on contrast-enhanced MRI and diffusion-weighted images. • The ratio of apparent diffusion coefficient values between the lesion and the spleen was significantly lower for the primary mass, liver metastases, and lymph node metastases of GB-NETs than for those of GB-ADCs. • A large metastatic lymph node was the only poor prognostic factor for overall survival in patients with GB-NETs and GB-ADCs. [ABSTRACT FROM AUTHOR]

Objectives: To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.Materials and Methods: Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28-81 years; 157 men [mean age, 60 years; range, 28-81 years] and 47 women [mean age, 54 years; range, 28-79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell's concordance index (C-index) based on patients' outcomes.Results: The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002).Conclusion: The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients' prognosis.Key Points: • This study investigated the value of deep learning dual-energy CT-based radiomics in predicting lymph node metastasis in gastric cancer. • The dual-energy CT-based radiomics nomogram outweighed the single-energy model and the clinical model. • The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies. [ABSTRACT FROM AUTHOR]

Objective: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists.Methods: First, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs.Results: The new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6.Conclusions: The study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm.Key Points: • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether the application of the Kaiser score for breast magnetic resonance imaging (MRI) might downgrade breast lesions that present as mammographic calcifications and avoid unnecessary breast biopsies METHODS: This IRB-approved, retrospective, cross-sectional, single-center study included 167 consecutive patients with suspicious mammographic calcifications and histopathologically verified results. These patients underwent a pre-interventional breast MRI exam for further diagnostic assessment before vacuum-assisted stereotactic-guided biopsy (95 malignant and 72 benign lesions). Two breast radiologists with different levels of experience independently read all examinations using the Kaiser score, a machine learning-derived clinical decision-making tool that provides probabilities of malignancy by a formalized combination of diagnostic criteria. Diagnostic performance was assessed by receiver operating characteristics (ROC) analysis and inter-reader agreement by the calculation of Cohen's kappa coefficients.Results: Application of the Kaiser score revealed a large area under the ROC curve (0.859-0.889). Rule-out criteria, with high sensitivity, were applied to mass and non-mass lesions alike. The rate of potentially avoidable breast biopsies ranged between 58.3 and 65.3%, with the lowest rate observed with the least experienced reader.Conclusions: Applying the Kaiser score to breast MRI allows stratifying the risk of breast cancer in lesions that present as suspicious calcifications on mammography and may thus avoid unnecessary breast biopsies.Key Points: • The Kaiser score is a helpful clinical decision tool for distinguishing malignant from benign breast lesions that present as calcifications on mammography. • Application of the Kaiser score may obviate 58.3-65.3% of unnecessary stereotactic biopsies of suspicious calcifications. • High Kaiser scores predict breast cancer with high specificity, aiding clinical decision-making with regard to re-biopsy in case of negative results. [ABSTRACT FROM AUTHOR]

Objectives: Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, as several research works have demonstrated in a pre-clinical setting, since it is able to provide attenuation, differential phase contrast, and scattering images simultaneously. In order to translate this technique to the clinics, it has to be adapted to cover a large field-of-view within a clinically acceptable exposure time and radiation dose.Methods: We set up a grating interferometer that fits into a standard mammography system and fulfilled the aforementioned conditions. Here, we present the first mastectomy images acquired with this experimental device.Results and Conclusion: Our system performs at a mean glandular dose of 1.6 mGy for a 5-cm-thick, 18%-dense breast, and a field-of-view of 26 × 21 cm2. It seems to be well-suited as basis for a clinical-environment device. Further, dark-field signals seem to support an improved lesion visualization. Evidently, the effective impact of such indications must be evaluated and quantified within the context of a proper reader study.Key Points: • Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, since it is sensitive to refraction and scattering and thus provides additional tissue information. • The most straightforward way to do grating-interferometry in the clinics is to modify a standard mammography device. • In a first approximation, the doses given with this technique seem to be similar to those of conventional mammography. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the utility of metabolic imaging, and specifically of dedicated breast positron emission tomography (dbPET) to differentiate between indolent and potentially aggressive ductal carcinoma in situ (DCIS).Methods: After institutional review board approval, we retrospectively reviewed the cases of pure DCIS who underwent dbPET before biopsy and surgery in Lucus Augusti Universitary Hospital (Lugo, Spain) and in Fudan Cancer Institute (Shanghai, China) between January 2016 and May 2018. Grade 1 and "non-comedo" grade 2 DCIS were considered low-risk disease, while intermediate-grade with necrosis or grade 3 cases were included in the high-risk group. DbPET sensitivity and specificity to differentiate between indolent and potentially aggressive DCIS were determined along with their respective 95% confidence intervals.Results: We enrolled 139 surgery-confirmed pure DCIS cases. Fifty were high-risk neoplasms and 89 low-risk DCIS. Only seven low-risk lesions were positive at dbPET and five of potentially aggressive neoplasms did not show FDG uptake, all included into the field of view (FOV). Sensitivity and specificity of dbPET to differentiate between indolent and potentially aggressive DCIS were 90% (95% CI, 77-96%) and 92% (95% CI, 84-97%), respectively.Conclusion: Metabolic imaging could help to identify the subgroup of indolent lesions from those potentially aggressive ones that may be managed by active surveillance.Key Points: • Low- and high-grade DCIS likely arise from two distinct evolutionary paths and when low-grade lesions progress to invasive cancer, the tumor is frequently low grade and well differentiated. • Ongoing clinical trials evaluate whether patients with low-risk DCIS could be safely managed by an active surveillance approach, with avoidance of unnecessary treatments and without impact on ipsilateral invasive breast cancer free survival time. • Dedicated breast PET may differentiate harmless from potentially hazardous DCIS, supporting active surveillance for the management of those women with low-grade DCIS, decreasing the rate of the upgrade to invasive carcinoma at surgical excision. [ABSTRACT FROM AUTHOR]

Objectives: Our study aimed at evaluating the prognostic implications of lung and mediastinal CT display window settings for solid portion measurements on the eighth-edition lung cancer staging system's clinical T (cT) categorization.Methods: We retrospectively analyzed 691 surgically treated patients from 2009 to 2015 for clinical stage IA lung adenocarcinomas. Solid portions were measured at the lung and mediastinal window settings, respectively, and cT categories were determined for each measurement (cTlung and cTmediastinum). The prognostic power of the two cT factors for disease-free survival (DFS) was assessed using Cox regression, and concordance indices (C-indices) were compared using the Student t test. Subsequently, the patients were split into training and validation cohorts to calculate optimal cutoffs for the cT categorization of mediastinal window-based solid portions (cToptimal) and validate its prognostic performance.Results: Both cTlung ((cT1b: adjusted HR, 3.547; p = 0.017), (cT1c: adjusted HR, 9.439; p < 0.001)) and cTmediastinum ((cT1b: adjusted HR, 4.635; p < 0.001), (cT1c: adjusted HR, 11.235; p < 0.001)) were significantly associated with DFS for each multivariable Cox model. The C-indices were 0.772 (95% CI, 0.702-0.842) for cTlung and 0.787 (95% CI, 0.726-0.848) for cTmediastinum (p = 0.789). The optimal cutoffs for cT categorization of the mediastinal window-based solid portions were 0.9 cm and 1.8 cm. However, there were no significant differences in the C-indices among cTlung, cTmediastinum, and cToptimal (p > 0.05).Conclusions: The prognostic performances of the cT categorizations at the lung and mediastinal windows were not significantly different. The current cT categorization based on the lung window measurement is appropriate as it stands.Key Points: • Discriminatory power of the eighth-edition clinical T category was not significantly affected by the CT display window settings. • Given the facts that the lung window setting enables more sensitive detection of the solid portions and higher correlation with the pathological invasive components, our findings may support adherence to the usage of the lung window setting for the solid portion measurement per the current recommendations. [ABSTRACT FROM AUTHOR]