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.

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.

Value of Internal Carotid Artery Stenosis in the Differential Diagnosis between Invasive Pituitary Adenoma and Invasive Meningioma.

Objective To assess the value of internal carotid artery stenosis in differentiating invasive pituitary adenoma (IPA) from invasive meningiomas (IM). Methods The clinical and imaging data of 28 IPA patients and 15 IM patients who were treated in our center from January 2012 to December 2016 were retrospectively analyzed. The magnetic resonance imaging (MRI) features were analyzed. The narrowest diameter (Dstenosis) and area (Astenosis) of internal carotid artery around the tumor were measured by computed tomography angiography (CTA),followed by the calculation of the stenosis score (%stenosis). The diagnostic validity of the measured indicators were calculated by receiver operating characteristic (ROC) curve. Results The median Ki-67 was 3% (2%-5%) in IPA group,which was significantly higher than that in IM group (1%,1%-2%) (Z=-3.983,P=0.000). The tumor texture showed significant differences between these two groups (P=0.001). While there was no significant difference in the average diameter [(39.63±13.15)mm in IPA group vs. (37.09±16.13)mm in IM group (t=0.518,P=0.607)],the shape (P=0.010),T1WI (P=0.001),signal (P=0.000),post-gadolinium enhancement (P=0.000),separation from normal pituitary (P=0.001),dural tail sign (P=0.000),and skull (P=0.001) showed significant differences. ROC analysis showed that the AUC of Dstenosis was 0.725 (P=0.006),the cut-off was 3.45 mm,the sensitivity was 62.50%,and the specificity was 76.47%;the AUC of Astenosis was 0.737 (P=0.003),the cut-off level was 11.00 mm2,the sensitivity was 75.00%,and the specificity was 64.71%;finally,the AUC of %stenosis was 0.711 (P=0.013),the cut-off level was 0.306,the sensitivity was 43.75%,and the specificity was 97.06%. Conclusions In addition to the common imaging features,the internal carotid artery stenosis is a valuable tool for differentiating IPA from IM. Three indicators including Dstenosis,Astenosis,and %stenosis have moderate diagnostic validity.

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.

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.

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.