The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes.

Background: Computed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes. Methods: From May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs. Results: In cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001). Conclusions: CT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.

Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm.

OBJECTIVES: To evaluate the clinical and non-contrast computed tomography (CT) features of patients with benign pulmonary subsolid nodules (SSNs) with a solid component ≤ 5 mm and their development trends via follow-up CT. METHODS: We retrospectively collected 436 data from patients who had SSNs with a solid component ≤ 5 mm, including 69 with absorbable benign SSNs (AB-SSNs), 70 with nonabsorbable benign SSNs (NB-SSNs), and 297 with malignant SSNs (M-SSNs). Models 1, 2, and 3 for distinguishing the different types of SSNs were then developed and validated. RESULTS: Patients with AB-SSNs were younger and exhibited respiratory symptoms more frequently than those with M-SSNs. The frequency of nodules detected during follow-up CT was in the following order: AB-SSNs > NB-SSNs > M-SSNs. NB-SSNs were smaller than M-SSNs, and ill-defined margins were more frequent in AB-SSNs than in NB-SSNs and M-SSNs. Benign SSNs exhibited irregular shape, target sign, and lower CT values more frequently compared to M-SSNs, whereas the latter demonstrated bubble lucency more commonly compared to the former. Furthermore, AB-SSNs showed more thickened interlobular septa and satellite lesions than M-SSNs and M-SSNs had more pleural retraction than AB-SSNs (all p < 0.017). The three models had AUCs ranging 0.748-0.920 and 0.790-0.912 in the training and external validation cohorts, respectively. A follow-up CT showed nodule progression in four benign SSNs. CONCLUSIONS: The three SSN types have different clinical and imaging characteristics, with some benign SSNs progressing to resemble malignancy. CRITICAL RELEVANCE STATEMENT: A good understanding of the imaging features and development trends of benign SSNs may help reduce unnecessary follow-up or interventions. This retrospective study explores the CT characteristics of benign SSNs with a solid component ≤ 5 mm by comparing AB-SSNs, NB-SSNs, and M-SSNs and delineates their development trends via follow-up CT. KEY POINTS: 1. Different subsolid nodule types exhibit distinct clinical and imaging features. 2. A miniscule number of benign subsolid nodules can progress to resemble malignancy. 3. Knowing the clinical and imaging features and development trends of benign subsolid nodules can improve management.

Solitary lung adenocarcinoma: follow-up CT, pathological-molecular characteristics, and surgical prognosis for different morphological classifications.

OBJECTIVE: To investigate the dynamic changes during follow-up computed tomography (CT), histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC). MATERIALS AND METHODS: This retrospective study compared dynamic tumor changes and volume doubling time (VDT) in 228 patients with SLADC (morphological types I-IV) who had intermittent growth during follow-ups. The correlation between the morphological classification and histological subtypes, gene mutation status, and surgical prognosis was evaluated. RESULTS: Among the 228 patients, 66 (28.9%) were classified as type I, 123 (53.9%) as type II, 16 (7%) as type III, and 23 (10.1%) as type IV. Type I had the shortest VDT (254 days), followed by types IV (381 days) and III (501 days), and then type II (993 days) (p < 0.05 each). Type I had a greater proportion of solid/micropapillary-predominant pattern than type II, and the lepidic-predominant pattern was more common in type II and III than in type I (p < 0.05 each). Furthermore, type II and IV SLADCs were correlated with positive epidermal growth factor receptor mutation (p < 0.05 each). Lastly, the Kaplan-Meier curves showed that the disease-free survival was longest for patients with type II tumors, followed by those with type III and IV tumors, and then those with type I tumors (p < 0.001 each). CONCLUSION: A good understanding of the natural progression and pathological-molecular characteristics of different morphological SLADC types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes. CRITICAL RELEVANCE STATEMENT: A good understanding of the natural progression and pathological-molecular characteristics of different morphological solitary lung adenocarcinoma types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes. KEY POINTS: • Type I-IV solitary lung adenocarcinomas exhibit varying natural progression on serial CT scans. • Morphological classification of solitary lung adenocarcinomas predicts histological subtype, gene status, and surgical prognosis. • This classification of solitary lung adenocarcinomas may help improve diagnostic, therapeutic, and prognosticating abilities.

Development and validation of a CT algorithm for the identification of nonperforated duodenal bulb ulcer.

PURPOSE: To assess the value of multiplanar computed tomography (CT) in the diagnosis of nonperforated duodenal bulb ulcer (NPDBU). METHOD: We retrospectively analyzed data from 135 patients with NPDBU (ulcer group) and 150 patients with a normal duodenal bulb (control group) who underwent contrast-enhanced abdominal CT and were diagnosed via upper endoscopy from January 2018 to February 2022. The clinical and CT features were compared between the two groups. Independent prognostic factors for diagnosing NPDBU were determined using binary logistic regression analysis. An external validation cohort to determine the model's efficiency comprised 80 patients from another center. RESULTS: Gastrointestinal bleeding was more frequent in patients with NPDBU than in those without (p < 0.001). No significant differences in age and sex were observed between the groups (all p > 0.05). The duodenal bulbar wall was significantly thicker in the ulcer group than in the control group, as determined using CT (p < 0.001). Irregular mucosal surface, layered enhancement, and blurred fat space around the duodenal bulb were more common in the ulcer group than in the control group (all p < 0.001). Binary logistic regression analysis revealed that gastrointestinal bleeding, wall thickness of ≥ 4.85 mm, irregular mucosal surface, and blurred peripheral fat space were the most significant variations associated with NPDBU, with an area under the curve (AUC) of 0.974. The external validation cohort had an AUC of 0.916. CONCLUSIONS: Careful multiplanar CT interpretation suggests the underlying presence of NPDBU and allows timely endoscopic verification and appropriate treatment.

Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

Background: To investigate the value of computed tomography (CT)-based radiomics signatures in combination with clinical and CT morphological features to identify epidermal growth factor receptor (EGFR)-mutation subtypes in lung adenocarcinoma (LADC). Methods: From February 2012 to October 2019, 608 patients were confirmed with LADC and underwent chest CT scans. Among them, 307 (50.5%) patients had a positive EGFR-mutation and 301 (49.5%) had a negative EGFR-mutation. Of the EGFR-mutant patients, 114 (37.1%) had a 19del -mutation, 155 (50.5%) had a L858R-mutation, and 38 (12.4%) had other rare mutations. Three combined models were generated by incorporating radiomics signatures, clinical, and CT morphological features to predict EGFR-mutation status. Patients were randomly split into training and testing cohorts, 80% and 20%, respectively. Model 1 was used to predict positive and negative EGFR-mutation, model 2 was used to predict 19del and non-19del mutations, and model 3 was used to predict L858R and non-L858R mutations. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate their performance. Results: For the three models, model 1 had AUC values of 0.969 and 0.886 in the training and validation cohorts, respectively. Model 2 had AUC values of 0.999 and 0.847 in the training and validation cohorts, respectively. Model 3 had AUC values of 0.984 and 0.806 in the training and validation cohorts, respectively. Conclusion: Combined models that incorporate radiomics signature, clinical, and CT morphological features may serve as an auxiliary tool to predict EGFR-mutation subtypes and contribute to individualized treatment for patients with LADC.

Combined model of radiomics, clinical, and imaging features for differentiating focal pneumonia-like lung cancer from pulmonary inflammatory lesions: an exploratory study.

BACKGROUND: Only few studies have focused on differentiating focal pneumonia-like lung cancer (F-PLC) from focal pulmonary inflammatory lesion (F-PIL). This exploratory study aimed to evaluate the clinical value of a combined model incorporating computed tomography (CT)-based radiomics signatures, clinical factors, and CT morphological features for distinguishing F-PLC and F-PIL. METHODS: In total, 396 patients pathologically diagnosed with F-PLC and F-PIL from two medical institutions between January 2015 and May 2021 were retrospectively analyzed. Patients from center 1 were included in the training (n = 242) and internal validation (n = 104) cohorts. Moreover, patients from center 2 were classified under the external validation cohort (n = 50). The clinical and CT morphological characteristics of both groups were compared first. And then, a clinical model incorporating clinical and CT morphological features, a radiomics model reflecting the radiomics signature of lung lesions, and a combined model were developed and validated, respectively. RESULTS: Age, gender, smoking history, respiratory symptoms, air bronchogram, necrosis, and pleural attachment differed significantly between the F-PLC and F-PIL groups (all P < 0.05). For the clinical model, age, necrosis, and pleural attachment were the most effective factors to differentiate F-PIL from F-PLC, with the area under the curves (AUCs) of 0.838, 0.819, and 0.717 in the training and internal and external validation cohorts, respectively. For the radiomics model, five radiomics features were found to be significantly related to the identification of F-PLC and F-PIL (all P < 0.001), with the AUCs of 0.804, 0.877, and 0.734 in the training and internal and external validation cohorts, respectively. For the combined model, five radiomics features, age, necrosis, and pleural attachment were independent predictors for distinguishing between F-PLC and F-PIL, with the AUCs of 0.915, 0.899, and 0.805 in the training and internal and external validation cohorts, respectively. The combined model exhibited a better performance than had the clinical and radiomics models. CONCLUSIONS: The combined model, which incorporates CT-based radiomics signatures, clinical factors, and CT morphological characteristics, is effective in differentiating F-PLC from F-PIL.

Differential diagnosis of localized pneumonic-type lung adenocarcinoma and pulmonary inflammatory lesion.

BACKGROUND: In clinical practice, a number of delayed diagnoses of localized pneumonic-type lung adenocarcinoma (L-PLADC) mimicking pneumonia have been identified due to the lack of knowledge regarding the radiological findings associated with this condition. Here, we defined L-PLADC as a special type of lung adenocarcinoma that presents as a focal consolidation involving < 50% of the area of a lobe and aimed to investigate the differential clinical and imaging features between L-PLADC and localized pulmonary inflammatory lesion (L-PIL). RESULTS: The data of 120 patients with L-PLADC and 125 patients with L-PIL who underwent contrast-enhanced chest computed tomography (CT) scan were retrospectively analyzed. For clinical characteristics, older age, women, nonsmokers, and no symptom were more common in L-PLADC (all p < 0.001). With regard to CT features, air bronchogram, irregular air bronchogram, ground-glass opacity (GGO) component, and pleural retraction were more frequently observed in L-PLADC, while necrosis, satellite lesions, halo sign, bronchial wall thickening, interlobular septa thickening, pleural attachment, and pleural thickening were more commonly seen in L-PIL (all p < 0.001). Multivariate analysis showed age ≥ 58 years, female sex, GGO component, irregular air bronchogram, pleural retraction, and the absence of necrosis and pleural attachment were the most effective variations associated with L-PLADC with an AUC of 0.979. Furthermore, an external validation cohort containing 62 patients obtained an AUC of 0.929. CONCLUSIONS: L-PLADC and L-PIL have different clinical and imaging characteristics. An adequate understanding of these differential features can contribute to the early diagnosis of L-PLADC and the subsequent therapeutic strategy.

Differential Diagnosis of Nonabsorbable Inflammatory and Malignant Subsolid Nodules with a Solid Component ≤5 mm.

Purpose: To investigate the differential clinical and computed tomography (CT) characteristics of pulmonary nonabsorbable inflammatory and malignant subsolid nodules (SSNs) with a solid component ≤5 mm. Patients and Methods: We retrospectively analyzed 576 consecutive patients who underwent surgical resection and had SSNs with a solid component ≤5 mm on CT images. These patients were divided into inflammatory and malignant groups according to pathology. Their clinical and imaging data were analyzed and compared. Multiple logistic regression analysis was used to identify independent prognostic factors differentiating inflammatory from malignant SSNs. Furthermore, 146 consecutive patients were included as internal validation cohort to test the prediction efficiency of this model. Results: Significant differences in 11 clinical characteristics and CT features were found between both groups (P < 0.05). Presence of respiratory symptoms, distribution of middle/lower lobe, irregular shape, part-solid nodule (PSNs), CT value of ground-glass opacity (GGO) areas <-657 Hu, presence of abnormal intra-nodular vessel sign, and interlobular septal thickening were the most effective factors for diagnosing nonabsorbable inflammatory SSNs, with an AUC (95% CI), accuracy, sensitivity, and specificity of 0.843 (95% CI: 0.811-0.872), 89.76%, 72.86%, and 81.23%, respectively. The internal validation cohort obtained an AUC (95% CI), accuracy, sensitivity, and specificity of 0.830 (95% CI: 0.759-0.887), 83.56%, 73.91%, and 76.42%, respectively. Conclusion: Nonabsorbable inflammatory and malignant SSNs with a solid component ≤5 mm exhibited different clinical and imaging characteristics.

Radiological classification, gene-mutation status, and surgical prognosis of synchronous multiple primary lung cancer.

OBJECTIVE: To investigate the radiological classification, gene-mutation status, and surgical prognosis of synchronous multiple primary lung cancer (sMPLC). METHODS: From January 2013 to October 2019, 192 consecutive patients with sMPLC were investigated. The clinical, CT, molecular, and pathological features of all patients were analyzed. Furthermore, the prognosis of 89 patients who only underwent surgical resection was evaluated. RESULTS: Among 192 patients, all lesions pathologically confirmed or highly suspected as tumors based on radiological findings were retrospectively analyzed, and the CT findings of sMPLC were classified into three types: (I) all lesions manifested as solid nodules/masses (14.06%, 27/192), (II) all lesions manifested as subsolid nodules/masses (43.23%, 83/192), and (III) tumor lesions manifested as a combination of ≥ 2 of the following patterns: solid nodules/masses, subsolid nodules/masses, cystic airspace, and focal consolidation (42.71%, 82/192). For 252 tumors undergoing epidermal growth factor receptor (EGFR)-mutation testing, the EGFR-mutation rate was higher in subsolid tumors than that in solid tumors (p < 0.05). Among 19 patients with all tumors undergoing surgery and driver-gene testing, genetic heterogeneity was prevalent among the multiple tumors (63.16%,12/19). The highest clinical stage of non-I, ipsilateral distribution of tumors, and CT classification of I indicated a poor prognosis for patients with sMPLC (all p < 0.05). CONCLUSION: Subsolid lesions are the most common presentation of sMPLC. Genetic heterogeneity in driver mutations among sMPLC may be present. Prognosis in patients with sMPLC is determined by the highest clinical TNM stage, distribution, and radiological classification among the multiple tumors. KEY POINTS: • Synchronous multiple primary lung cancer (sMPLC) has three types of CT findings. • Genetic heterogeneity may be prevalent among the multiple tumors. • Prognosis in patients with sMPLC is associated with the highest clinical TNM stage, distribution, and radiological classification among the multiple tumors.

Pneumonic-type lung adenocarcinoma with different ranges exhibiting different clinical, imaging, and pathological characteristics.

BACKGROUND: Pneumonic-type lung adenocarcinoma (PLADC) with different ranges might exhibit different imaging and clinicopathological features. This study divided PLADC into localized PLADC (L-PLADC) and diffuse PLADC (D-PLADC) based on imaging and aimed to clarify the differences in clinical, imaging, and pathologic characteristics between the two new subtypes. RESULTS: The data of 131 patients with L-PLADC and 117 patients with D-PLADC who were pathologically confirmed and underwent chest computed tomography (CT) at our institute from December 2014 to December 2020 were retrospectively collected. Patients with L-PLADC were predominantly female, non-smokers, and without respiratory symptoms and elevated white blood cell count and C-reactive protein level, whereas those with D-PLADC were predominantly male, smokers, and had respiratory symptoms and elevated white blood cell count and C-reactive protein level (all p < 0.05). Pleural retraction was more common in L-PLADC, whereas interlobular fissure bulging, hypodense sign, air space, CT angiogram sign, coexisting nodules, pleural effusion, and lymphadenopathy were more frequent in D-PLADC (all p < 0.001). Among the 129 patients with surgically resected PLADC, the most common histological subtype of L-PLADC was acinar-predominant growth pattern (76.7%, 79/103), whereas that of D-PLADC was invasive mucinous adenocarcinoma (80.8%, 21/26). Among the 136 patients with EGFR mutation status, L-PLADC had a significantly higher EGFR mutation rate than D-PLADC (p < 0.001). CONCLUSIONS: L-PLADC and D-PLADC have different clinical, imaging, and pathological characteristics. This new imaging-based classification may help improve our understanding of PLADC and develop personalized treatment plans, with concomitant implications for patient outcomes.

Clinicopathological and computed tomographic features associated with occult lymph node metastasis in patients with peripheral solid non-small cell lung cancer.

OBJECTIVES: To investigate the value of combining clinicopathological characteristics with computed tomographic (CT) features of tumours for predicting occult lymph node metastasis (OLNM) in peripheral solid non-small cell lung cancer (PS-NSCLC). METHODS: The study included 478 NSCLC clinically N0 (cN0) patients who underwent lobectomy and systemic lymph node dissection from January 2014 to August 2019. Patients were classified into OLNM and negative lymph node metastasis (NLNM) groups. The CT features of non-metastatic and metastatic lymph nodes with a largest short-diameter > 5 mm were compared in the OLNM group. Thereafter, the clinicopathological characteristics and CT morphological features of tumours were compared between both groups. Multivariable logistic regression analysis and receiver-operating characteristic curve were developed. RESULTS: CT images detected 103 metastatic and 705 non-metastatic lymph nodes, and no significant differences in CT features of lymph nodes were found in all 161 OLNM patients (P > 0.05). For both groups, sex, carcinoembryonic antigen and pathological type differed significantly (all P < 0.05), while tumour size, necrosis, calcification, vascular convergence, pleural involvement, and the shortest interval of tumour-pleura differed significantly on CT images (all P < 0.05). Multivariable logistic regression analysis showed that carcinoembryonic antigen > 5.00 ng/ml, adenocarcinoma, absence of vascular convergence, and pleural involvement of Type II (one linear or cord-like pleural tag or tumour abut to the pleura with a broad base observed on both lung and mediastinal window images) were independent predicting factors of OLNM. CONCLUSIONS: CT findings of lymph nodes can provide limited value and integrating clinicopathological characteristics with the CT morphological features of tumours is helpful in predicting OLNM in patients with PS-NSCLC.

Computed Tomography Morphological Classification of Lung Adenocarcinoma and Its Correlation with Epidermal Growth Factor Receptor Mutation Status: A Report of 1075 Cases.

BACKGROUND: Many delayed diagnoses of lung adenocarcinoma (LADC) are identified due to poor understanding of protean imaging findings. Moreover, clarifying the relationship between computed tomography (CT) morphological classification and epidermal growth factor receptor (EGFR) mutations of LADC might inform therapeutic decision-making while obtaining pathological specimens is difficult. Here, we retrospectively analyzed CT manifestations of LADC and investigated the morphological classification of tumors in relation to EGFR mutation status. METHODS: We included 1075 LADC patients undergoing chest CT and EGFR genotype examinations from January 2013 to January 2019. CT morphological characteristics of tumors were carefully evaluated and their correlation with EGFR mutation status was analyzed using the chi-squared test. RESULTS: Tumors were divided into eight types: I (peripheral solid nodule/mass; 526/1075, 48.93%), II (central solid nodule/mass; 220/1075, 20.47%), III (subsolid nodule/mass; 92/1075, 8.56%), IV (focal consolidation; 32/1075, 2.98%), V (cystic airspace; 14/1075, 1.30%), VI (multiple lesions with similar appearances to I-V; 85/1075, 7.91%), VII (diffuse consolidation; 53/1075, 4.93%), VIII (occult lesion usually obscured by nonobstructive atelectasis; 53/1075, 4.93%). Type III and IV tumors were more frequent in patients with EGFR mutation, whereas type II and VII tumors were more common in patients without EGFR mutation (all P < 0.05). However, we did not identify any significant associations between other tumor types and EGFR mutation status (all P > 0.05). Among patients with type VI tumors, EGFR mutation status was closely related to tumor density (all P < 0.05). Furthermore, type VII tumors were associated with 19 deletion mutation positive and non-L858R mutation positive (all P < 0.05). CONCLUSION: LADC can be categorized into eight types based on CT imaging. Improving our understanding of the morphological classification and correlation with EGFR mutation status may contribute to the accurate diagnosis of LADC, while suggesting the presence of underlying EGFR genetic mutations.

Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma.

BACKGROUND: Based on the "seed and soil" theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC). METHODS: Radiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (-) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model's performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed. RESULTS: Thirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691-0.927 in the training cohort and 0.700-0.951 in the validation cohort, respectively, thereby indicating good performance. CONCLUSION: CT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.

Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography.

BACKGROUND: Necrotic pulmonary lesions manifest as relatively low-density internally on contrast-enhanced computed tomography (CT). However, using CT to differentiate malignant and benign necrotic pulmonary lesions is challenging, as these lesions have similar peripheral enhancement. With the introduction of dual-energy spectral CT (DESCT), more quantitative parameters can be obtained and the ability to differentiate material compositions has been highly promoted. This study investigated the use of kVp-switching DESCT in differentiating malignant from benign necrotic lung lesions. METHODS: From October 2016 to February 2019, 40 patients with necrotic lung cancer (NLC) and 31 with necrotic pulmonary mass-like inflammatory lesion (NPMIL) were enrolled and underwent DESCT. The clinical characteristics of patients, CT morphological features, and DESCT quantitative parameters of lesions were compared between the two groups. Binary logistic regression analysis was performed to identify the independent prognostic factors differentiating NPMIL from NLC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analyses. RESULTS: Significant differences in age, C-reactive protein concentration, the slope of the spectral curve from 40 to 65 keV (K40-65 keV) of necrosis in non-contrast-enhanced scanning (NCS), arterial phase (AP) and venous phase (VP), effective atomic number of necrosis in NCS, and iodine concentration (IC) of the solid component in VP were observed between groups (all p < 0.05). The aforementioned parameters had area under the ROC curve (AUC) values of 0.747, 0.691, 0.841, 0.641, 0.660, 0.828, and 0.754, respectively, for distinguishing between NLC and NPMIL. Multiparametric analysis showed that age, K40-65 keV of necrosis in NCS, and IC of the solid component in VP were the most effective factors for differentiating NLC from NPMIL, with an AUC of 0.966 and percentage of correct class of 88.7%. CONCLUSIONS: DESCT can differentiate malignant from benign necrotic lung lesions with a relatively high accuracy.

Spectral CT in Lung Cancer: Usefulness of Iodine Concentration for Evaluation of Tumor Angiogenesis and Prognosis.

OBJECTIVE. The purpose of this study was to investigate the correlation between iodine concentration (IC) derived from spectral CT and angiogenesis and the relationships between IC and clinical-pathologic features associated with lung cancer prognosis. SUBJECTS AND METHODS. Sixty patients with lung cancer were enrolled and underwent spectral CT. The IC, IC difference (ICD), and normalized IC (NIC) of tumors were measured in the arterial phase, venous phase (VP), and delayed phase. The microvessel densities (MVDs) of CD34-stained specimens were evaluated. Correlation analysis was performed for IC and MVD. The relationships between the IC index showing the best correlations with MVD and clinical-pathologic findings of pathologic types, histologic differentiation, tumor size, lymph node status, pathologic TNM stage, and intratumoral necrosis were investigated. RESULTS. The mean (± IQR) MVD of all tumors was 42.00 ± 27.50 vessels per field at ×400 magnification, with two MVD distribution types. The MVD of lung cancer correlated positively with the IC, ICD, and NIC on three-phase contrast-enhanced scanning (r range, 0.581-0.800; all p < 0.001), and the IC in the VP showed the strongest correlation with MVD (r = 0.800; p < 0.001). The correlations between IC and MVD, ICD and MVD, and NIC and MVD varied depending on whether the same scanning phase or same IC index was used. The IC in the VP showed statistically significant differences in the pathologic types of adenocarcinoma and squamous cell carcinoma, histologic differentiation, tumor size, and status of intratumoral necrosis of lung cancer (p < 0.05), but was not associated with nodal metastasis and pathologic TNM stages (p > 0.05). CONCLUSION. IC indexes derived from spectral CT, especially the IC in the VP, were useful indicators for evaluating tumor angiogenesis and prognosis.

Assessment the reliability of ultrasonography in the imaging of the plantar fascia: a comparative study.

BACKGROUND: Imaging methods for the plantar fascia have included radiography, ultrasound and magnetic resonance imaging (MRI), all of which have provided valuable information. This study assessed the reliability of ultrasonography examinations of the plantar fascia using a comparative study. METHODS: Fifty healthy adult volunteers (25 males and 25 females, mean age 31.6 ± 3.5 years) were included in this study. Images of the plantar fascia from 100 ft were acquired with ultrasonography, CT and MRI. Ultrasound was used to measure the thickness of the plantar fascia. Imaging data from CT and MRI in a DICOM format were transformed into the Materialise Mimics Innovation Suite 16.0 software for digital analysis. SPSS software (SPSS, USA) was used for statistical analysis. The reliability was established by a t-test. Moreover, 42 patients with unilateral plantar fasciitis were examined by ultrasonography. RESULTS: There were no significant differences between the three imaging modalities for patients of the same sex (P > 0.05). There were no statistically significant differences between the left and right sides for patients of the same sex (P > 0.05), but the difference between males and females was statistically significant (P < 0.01). There were no significant differences between US, CT and MRI in the normal group, but there were significant differences in the plantar fasciitis group evaluated with ultrasound. The plantar fascii of normal male subjects are significantly thicker than those of the normal female. CONCLUSION: Ultrasonography can be a relatively simple and reliable method for the measurement of plantar fascia thickness.

MRI features of intracranial anaplastic hemangiopericytoma.

Magnetic resonance imaging (MRI) features of intracranial anaplastic hemangiopericytoma (AHPC) were analyzed. The pathological examination showed that there was a great number of irregularly arranged tumor cells with nuclear atypia, and mitotic properties were commonly seen providing support for clinical staging, therapy and prognosis judgment. Eighteen cases of intracranial AHPC proved by operation and pathology were analyzed retrospectively. Both plain and enhanced MR scans were performed and the results were compared with pathology in all cases. In all 18 cases, the tumor was positioned in the cortex; in 12 cases, it was located in the frontal falx and in 3 cases, it was located in the parietal falx. In 2 cases, the tumor was located in the middle cranial fossa, and in 1 case, it was located in the cerebellar hemispheres. Thirteen of the 18 cases showed mixed hyper-iso signal intensity with cortical grey matter, and the other 5 cases were isointense in the cortical grey matter on T1-weighted images. Fifteen of the 18 cases showed heterogeneous hyper-iso signal intensity, and the other 3 cases were isointense on T2-weighted images. Fifteen of the 18 cases showed heterogeneous enhancement in contrast-enhanced T1-weighted images. Our data show that, because intracranial AHPC has specific features on MRI, it could be very useful for its clinical diagnosis.

Abnormal spontaneous brain activity in type 2 diabetes with and without microangiopathy revealed by regional homogeneity.

PURPOSE: To investigate whether global spontaneous brain activity changes in type 2 diabetes mellitus (T2DM) patients and these changes vary according to the degree of microangiopathy. MATERIALS AND METHODS: T2DM patients with (M(+), n=26) and without (M(-), n=22) microangiopathy as well as 28 healthy nondiabetic subjects were enrolled in this study. All the subjects completed a resting-state functional magnetic resonance imaging (rs-fMRI) examination and neuropsychological assessment. Regional homogeneity (ReHo) values, representing spontaneous brain activity, were calculated and compared between M(+) and M(-) T2DM patients and nondiabetic controls. RESULTS: In both M(+) and M(-) T2DM patients, ReHo values were decreased in the occipital lobe, temporal lobe, postcentral gyrus, and cerebellum, while increased in the bilateral precuneus, superior/middle frontal gyrus, and insula. Compared with the M(-) group, M(+) patients showed decreased ReHo values in the left cuneus and superior occipital gyrus. The ReHo values in the lingual gyrus/calcarine cortex and MTG were related to clinical parameters in T2DM patients. CONCLUSION: The abnormalities of spontaneous brain activity revealed by ReHo values in both M(+)and M(-) T2DM patients may provide insights into the neurological pathophysiology underlying diabetes-related cognitive impairments. M(+) patients showed more decreased brain activity related to severely impaired function of visual processing and visual memory.

Tree-in-bud pattern in central lung cancer: CT findings and pathologic correlation.

OBJECTIVES: Tree-in-bud (TIB) pattern can be found with central lung cancer on chest CT scans. However, few reports have described it so far. We aim to determine its incidence, CT findings and pathologic basis. MATERIALS AND METHODS: 652 consecutive patients with confirmed central lung cancer were enrolled in our study. The incidence, CT findings and pathologic features of TIB pattern were analyzed retrospectively. RESULTS: In total, TIB pattern was found in 22.5% (147/652) of patients. It was more common in patients with squamous cell carcinoma than those with non-squamous cell carcinoma (P=0.000). The most important associated CT finding was obstructive bronchial mucoid impaction distal to the cancer (100%, 147/147), followed by consolidation and ground-glass opacities (62.6%, 92/147). TIB pattern was confined to the areas of lung supplied by the involved bronchi (100%, 147/147) and had a focal distribution predominantly (94.6%, 139/147). Pathologically, it correlated with bronchiolectasis, bronchiolar lumen filled by mucus and inflammatory exudates, wall thickening with inflammatory cells infiltration. CONCLUSIONS: TIB pattern is commonly detected in central lung squamous cell carcinoma on thoracic CT scan. It usually has a localized distribution with a predominant associated CT finding of obstructive bronchial mucoid impaction. This pattern corresponds to the mucoid impaction of bronchioles and bronchiolitis pathologically. A full understanding of TIB pattern in central lung cancer can be useful in preventing diagnostic errors.

[Analysis of the immunohistochemical subtypes and prognosis of primary diffuse large B cell lymphoma of the central nervous system].

OBJECTIVE: To analyze the subtypes of primary diffuse large B cell lymphoma of the central nervous system (CNS DLBCL) and to explore the relationship between the subtype classification and prognosis. METHODS: Immunohistochemical staining was used to determine the expression of CD20, CD3, CD10, Bcl-6, Mum-1, CD5, Bcl-2, Ki-67, FOXP-1, GCET-1, BLIMP-1 and LMO-2 antigens on paraffin-embedded sections of 47 cases. Hans, Choi and Tally subtypes were classified, and univariate and multivariate analyses were used to elucidate the relationship between the subtypes and prognosis. RESULTS: In the 47 cases, the expression of Bcl-2 in the tumor cells was 46.8%, CD10 4.3%, Bcl-6 70.2%, Mum-1 53.2%, GCET-1 36.2%, BLIMP-1 4.3%, FOXP-1 63.8% and LMO-2 19.2%. The positive rate of Ki-67 was 30% to 95%, with a median of 80%, of which 12 cases (25.5%) was > or = 90%. The Hans subtype classification showed 16 cases (34.0%) were of GCB type and 31 cases (66.0%) of non-GCB type. The Choi subtype classification showed 16 cases (34.0%) were of GCB type and 31 cases (66.0%) of ABC type. The Tally subtype classification showed 6 cases (12.8%) were of GCB type and 41 cases (87.2%) of non-GCB type. CONCLUSIONS: The results of this study show that there is no significant correlation between the three subtypes and prognosis. The prognosis is correated with post-operative radiotherapy, chemotherapy and MTX therapy.