Radiological Features of Primary Pulmonary Invasive Mucinous Adenocarcinoma Based on 312 Consecutive Patients.

BACKGROUND: The aim of this study is to investigate the radiological features of primary pulmonary invasive mucinous adenocarcinoma (IMA) in a relatively large population to help improve its further understanding and its accuracy of initial diagnosis. METHODS: This retrospective study included consecutive patients with pathologically confirmed primary pulmonary IMA from January 2019 to December 2021. According to tumor morphology, IMAs were divided into regular nodule/mass, irregular, and large consolidative types. According to tumor density, IMAs were divided into solid, halo, part-solid, pure ground-glass, and cystic types. ANOVA, chi-square, or Fisher exact tests were used to analyze the differences in radiological and clinicopathological characteristics of IMA according to morphological and density subtypes. RESULTS: We analyzed 312 patients. Pulmonary IMA tended to occur in the elderly, with a slightly higher number of women than men. IMA showed a predominance in the lower lobe and adjacent to pleura. IMA of regular nodule/mass, irregular, and large consolidative types accounted for 80.8% (252/312), 13.8% (43/312), and 5.4% (17/312), respectively. Solid, halo, part-solid, pure ground-glass, and cystic IMAs accounted for 55.8% (174/312), 28.2% (88/312), 11.2% (35/312), 1.3% (4/312), and 3.5% (11/312), respectively. The lobulated (76.9%), spiculated (63.5%), and air bronchogram (56.7%) signs were common in IMA. Dead branch sign (88.2%), angiogram sign (88.2%), and satellite nodules/skipping lesions (47.1%) were common in large-consolidative-type IMA. Kirsten rat sarcoma viral oncogene mutations were common (56.1%), whereas epidermal growth factor receptor mutations were relatively rare (2.3%). CONCLUSIONS: Pulmonary IMA of regular nodule/mass type and solid type were the most common at the initial diagnosis. Detailed radiological features can aid in the differential diagnosis of IMA.

A prediction model based on computed tomography characteristics for identifying malignant from benign sub-centimeter solid pulmonary nodules.

Background: Distinguishing benign from malignant sub-centimeter solid pulmonary nodules (SSPNs) continues to be challenging in clinical practice. Earlier diagnosis is crucial for improving patient survival and prognosis. This study aimed to investigate the risk factors of malignant SSPNs and establish and validate a prediction model based on computed tomography (CT) characteristics to assist in their early diagnosis. Methods: A total of 261 consecutive participants with 261 SSPNs were retrospectively recruited between January 2012 and July 2023 from National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (Center 1), including 161 malignant lesions and 100 benign lesions. Patients were randomly assigned to the training set (n=183) and validation set (n=78) according to a 7:3 ratio. Malignant nodules were confirmed by pathology; and benign nodules were confirmed by follow-up or pathology. Clinical data and CT features were collected to estimate the independent predictors of malignancy of SSPN with multivariate logistic analysis. A clinical prediction model was subsequently established by logistic regression. Furthermore, an additional 69 consecutive patients with 69 SSPNs from The Fourth Hospital of Hebei Medical University (Center 2) between January 2022 and December 2022 were retrospectively included as an external cohort to validate the predictive efficacy of the model. The performance of the prediction model was assessed by sensitivity, specificity, and the area under the receiver operating characteristic curve. Results: There were 113 (61.7%), 48 (61.5%) and 28 (40.6%) malignant SSPNs in the training, internal and external validation sets, respectively. Multivariate logistic analysis revealed four independent predictors of malignant SSPNs: tumor-lung interface (P=0.002), spiculation (P=0.04), air bronchogram (P=0.047), and invisible at the mediastinal window (P=0.003). The area under the curve (AUC) for the prediction model in the training set was 0.875 [95% confidence interval (CI): 0.818, 0.933]; and the sensitivity and specificity were 94.7% and 68.6%, respectively. The AUCs in the internal and external validation set were (0.781; 95% CI: 0.664, 0.897) and (0.873; 95% CI: 0.791, 0.955), respectively; the sensitivity and specificity were 66.7% and 83.3% for the internal validation data, and 100.0% and 61.0% for the external validation data, respectively. Conclusions: The prediction model based on CT characteristics could be helpful for distinguishing malignant SSPNs from benign ones.

A Self-supervised Learning-Based Fine-Grained Classification Model for Distinguishing Malignant From Benign Subcentimeter Solid Pulmonary Nodules.

RATIONALE AND OBJECTIVES: Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images. MATERIALS AND METHODS: This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model's efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. RESULTS: Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model's performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model's performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860. CONCLUSION: This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.

Predicting epidermal growth factor receptor mutations in non-small cell lung cancer through dual-layer spectral CT: a prospective study.

OBJECTIVE: To determine whether quantitative parameters of detector-derived dual-layer spectral computed tomography (DLCT) can reliably identify epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). METHODS: Patients with NSCLC who underwent arterial phase (AP) and venous phase (VP) DLCT between December 2021 and November 2022 were subdivided into the mutated and wild-type EGFR groups following EGFR mutation testing. Their baseline clinical data, conventional CT images, and spectral images were obtained. Iodine concentration (IC), iodine no water (INW), effective atomic number (Zeff), virtual monoenergetic images, the slope of the spectral attenuation curve (λHU), enhancement degree (ED), arterial enhancement fraction (AEF), and normalized AEF (NAEF) were measured for each lesion. RESULTS: Ninety-two patients (median age, 61 years, interquartile range [51, 67]; 33 men) were evaluated. The univariate analysis indicated that IC, normalized IC (NIC), INW and ED for the AP and VP, as well as Zeff and λHU for the VP were significantly associated with EGFR mutation status (all p < 0.05). INW(VP) showed the best diagnostic performance (AUC, 0.892 [95% confidence interval {CI}: 0.823, 0.960]). However, neither AEF (p = 0.156) nor NAEF (p = 0.567) showed significant differences between the two groups. The multivariate analysis showed that INW(AP) and NIC(VP) were significant predictors of EGFR mutation status, with the latter showing better performance (p = 0.029; AUC, 0.897 [95% CI: 0.816, 0.951] vs. 0.774 [95% CI: 0.675, 0.855]). CONCLUSION: Quantitative parameters of DLCT can help predict EGFR mutation status in patients with NSCLC. CRITICAL RELEVANCE STATEMENT: Quantitative parameters of DLCT, especially NIC(VP), can help predict EGFR mutation status in patients with NSCLC, facilitating appropriate and individualized treatment for them. KEY POINTS: Determining EGFR mutation status in patients with NSCLC before starting therapy is essential. Quantitative parameters of DLCT can predict EGFR mutation status in NSCLC patients. NIC in venous phase is an important parameter to guide individualized treatment selection for NSCLC patients.

Radiological and clinical features of large consolidative-type pulmonary invasive mucinous adenocarcinoma.

BACKGROUND: This study aimed to investigate the radiological, pathological, and prognostic characteristics of large consolidative-type pulmonary invasive mucinous adenocarcinomas (IMA). METHODS: We retrospectively reviewed 738 patients who confirmed IMA between January 2010 and August 2022, and two radiologists reviewed imaging data to determine subtypes. We included 41 patients with pathologically large consolidative-type IMA. We analyzed their radiological, pathological, and prognostic characteristics. The recurrence-free survival (RFS) and overall survival (OS) were determined using the Kaplan-Meier method. RESULTS: Most lesions were located in the lower lobe, with 46.3% patients showing multiple lesions. Halo, angiogram, vacuole, air bronchogram, and dead branch sign were observed in 97.6%, 73.2%, 63.4%, 61.0%, and 61.0% of the cases, respectively. Unevenly low enhancement was observed in 88.89% of patients. T3 and T4 pathological stages were observed in 50.0% and 30.6% of patients, respectively. Lymph node metastasis was observed in 16.7% patients, with no distant metastasis. Spread-through air spaces and intrapulmonary dissemination were observed in 27.8% and 19.4% patients, respectively. Moreover, Kirsten rat sarcoma viral oncogene mutations were found in 68.6% of cases, and no epidermal growth factor receptor mutations were seen. Among all mutation sites, G12V mutation is the most common, accounting for 40%. The average RFS and OS were 19.4 and 66.4 months, respectively, with 3-year RFS and OS rates of 30.0% and 75.0%, respectively. Pleural invasion and lymph node metastasis were independent risk factors for diagnosis. CONCLUSION: Halo, vacuole, angiogram, and dead branch signs were frequently observed in consolidative-type IMA. Kirsten rat sarcoma viral oncogene mutations are common in consolidative-type IMA, especially site G12V, whereas epidermal growth factor receptor mutations were rare; therefore, gene immunotherapy was more difficult. Most patients were in stage T3-T4; however, lymph node metastasis was rare.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules.

BACKGROUND: We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs). METHODS: A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value. CONCLUSIONS: The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs. RELEVANCE STATEMENT: The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work. KEY POINTS: • We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.

Generalization of the modulatory effect of social interaction on personal space.

Introduction: Personal space (PS) is a safe area around an individual's body that affects spatial distance when socially interacting with others. Previous studies have shown that social interaction may modulate PS. However, these findings are often confounded by the effects of familiarization. Furthermore, whether the potential regulatory effects of social interaction on PS can be generalized from interacting confederates to strangers remains unclear. Methods: To answer these questions, we enrolled 115 participants in a carefully designed experiment. Results: We found that prosocial interaction in the form of a cooperative task effectively reduced PS, and this regulatory effect could be generalized from interacting confederates to non-interacting confederates. Discussion: These findings deepen our understanding of PS regulation and may be aid in the diagnosis and rehabilitation of dysfunctional social behaviors.