Preoperative markers for identifying CT ≤2 cm solid nodules of lung adenocarcinoma based on image deep learning.

BACKGROUND: The solid pattern is a highly malignant subtype of lung adenocarcinoma. In the current era of transitioning from lobectomy to sublobar resection for the surgical treatment of small lung cancers, preoperative identification of this subtype is highly important for patient surgical approach selection and long-term prognosis. METHODS: A total of 1489 patients with clinical stage IA1-2 primary lung adenocarcinoma were enrolled. Based on patient clinical characteristics and lung imaging features obtained via deep learning, highly correlated diagnostic factors were identified through LASSO regression and decision tree analysis. Subsequently, a logistic model and nomogram were constructed. A restricted cubic spline (RCS) was used to calculate the optimal inflection point of quantitative data and the differences between the groups. RESULTS: The three-dimensional proportion of solid component (PSC), sex, and smoking status was identified as being highly correlated diagnostic factors for solid predominant adenocarcinoma. The logistic model had good prediction efficiency, and the area under the ROC curve was 0.85. Decision curve analysis demonstrated that the application of diagnostic factors can improve patient outcomes. RCS analysis indicated that the proportion of solid adenocarcinomas increased by 4.6 times when the PSC was ≥72%. A PSC of 72% is a good cutoff point. CONCLUSION: The preoperative diagnosis of solid-pattern adenocarcinoma can be confirmed by typical imaging features and clinical characteristics, assisting the thoracic surgeon in developing a more precise surgical plan.

A large benign struma ovarii with atypical imaging findings.

Struma ovarii is a mature ovarian teratoma characterized by the predominant presence of thyroid-tissue components. Typically, struma ovarii presents as relatively small masses (<10 cm) that often appear as multilocular cystic tumors with solid components. Herein, we report the unique case of a 44-year-old female with a large tumor including a solid mass. The solid components of the tumor comprised typical thyroid tissues with multiple small cystic structures containing colloid-like material. Given the rarity of struma ovarii, atypical imaging features can sometimes be misleading. This article highlights the unusual magnetic resonance imaging characteristics of a large struma ovarii, with a specific focus on the presence of solid components.

Enhancing identification of early-stage lung adenocarcinomas through solid component analysis of three-dimensional computed tomography images.

OBJECTIVES: As the role of segmentectomy expands in managing early-stage lung adenocarcinoma, precise preoperative assessments of tumor invasiveness via computed tomography become crucial. This study aimed to evaluate the effectiveness of solid component analysis of three-dimensional (3D) computed tomography images and establish segmentectomy criteria for early-stage lung adenocarcinomas. METHODS: This retrospective study included 101 cases with adenocarcinoma diagnoses, with patients undergoing segmentectomy for clinical stage 0 or IA between 2012 and 2017. The solid component volume (3D-volume) and solid component ratio (3D-ratio) of tumors were calculated using 3D computed tomography. Additionally, based on two-dimensional (2D) computed tomography, the solid component diameter (2D-diameter) and solid component ratio (2D-ratio) were calculated. The area under the receiver-operating characteristic curve (AUC) was calculated for each method, facilitating predictions of mortality and recurrence within 5 years. The AUC of each measurement was compared with those of invasive component diameter (path-diameter) and invasive component ratio (path-ratio) obtained through pathology analysis. RESULTS: The predictive performance of 3D-volume did not differ significantly from that of path-diameter, whereas 2D-diameter exhibited less predictive accuracy (AUC: 3D-volume, 2D-diameter, and path-diameter: 0.772, 0.624, and 0.747, respectively; 3D-volume vs. path-diameter: p = 0.697; 2D-diameter vs. path-diameter: p = 0.048). Results were similar for the solid component ratio (AUC: 3D-ratio, 2D-ratio, path-ratio: 0.707, 0.534, and 0.698, respectively; 3D-ratio vs. path-ratio: p = 0.882; 2D-ratio vs. path-ratio: p = 0.038). CONCLUSION: Solid component analysis using 3D computed tomography offers advantages in prognostic prediction for early-stage lung adenocarcinomas.

Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma.

OBJECTIVE: To assess the ability of markers of inflammation to identify the solid or micropapillary components of stage IA lung adenocarcinoma and their effects on prognosis. METHODS: We performed a retrospective study of clinicopathologic data from 654 patients with stage IA lung adenocarcinoma collected between 2013 and 2019. Logistic regression analysis was used to identify independent predictors of these components, and we also evaluated the relationship between markers of inflammation and recurrence. RESULTS: Micropapillary-positive participants had high preoperative neutrophil-to-lymphocyte ratios. There were no significant differences in the levels of markers of systemic inflammation between the participants with or without a solid component. Multivariate analysis showed that preoperative neutrophil-to-lymphocyte ratio (odds ratio [OR] = 2.094; 95% confidence interval [CI], 1.668-2.628), tumor size (OR = 1.386; 95% CI, 1.044-1.842), and carcinoembryonic antigen concentration (OR = 1.067; 95% CI, 1.017-1.119) were independent predictors of a micropapillary component. There were no significant correlations between markers of systemic inflammation and the recurrence of stage IA lung adenocarcinoma. CONCLUSIONS: Preoperative neutrophil-to-lymphocyte ratio independently predicts a micropapillary component of stage IA lung adenocarcinoma. Therefore, the potential use of preoperative neutrophil-to-lymphocyte ratio in the optimization of surgical strategies for the treatment of stage IA lung adenocarcinoma should be further studied.

Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.

PURPOSE: To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT). MATERIALS AND METHODS: For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant. RESULTS: Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists. CONCLUSION: Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard. METHODS: In this retrospective study, an AI algorithm was proposed for measuring the solid components ratio in SSNs, which was used to assess the diameter ratio (1D), area ratio (2D), and volume ratio (3D). The radiologist measured each SSN's consolidation to tumor ratio (CTR) twice, four weeks apart. The area under the receiver-operating characteristic (ROC) curve (AUC) was calculated for each method used to discriminate an Invasive Adenocarcinoma (IA) from a non-IA. The AUC and the time cost of each measurement were compared. Furthermore, we examined the consistency of measurements made by the radiologist on two separate occasions. RESULTS: A total of 379 patients (the primary dataset n = 278, the validation dataset n = 101) were included. In the primary dataset, compared to the manual approach (AUC: 0.697), the AI algorithm (AUC: 0.811) had better predictive performance (P =.0027) in measuring solid components ratio in 3D. Algorithm measurement in 3D had an AUC no inferior to 1D (AUC: 0.806) and 2D (AUC: 0.796). In the validation dataset, the AI 3D method also achieved superior diagnostic performance compared to the radiologist (AUC: 0.803 vs 0.682, P =.046). The two measurements of the CTR in the primary dataset, taken 4 weeks apart, have 7.9 % cases in poor consistency. The measurement time cost by the radiologist is about 60 times that of the AI algorithm (P <.001). CONCLUSION: The 3D measurement of solid components using AI, is an effective and objective approach to predict the pathological invasiveness of SSNs. It can be a preoperative interpretable indicator of pathological invasiveness in patients with lung adenocarcinoma.

Optimization of Indocyanine Green for Intraoperative Fluorescent Image-Guided Localization of Lung Cancer; Analysis Based on Solid Component of Lung Nodule.

ICG fluorescence imaging has been used to detect lung cancer; however, there is no consensus regarding the optimization of the indocyanine green (ICG) injection method. The aim of this study was to determine the optimal dose and timing of ICG for lung cancer detection using animal models and to evaluate the feasibility of ICG fluorescence in lung cancer patients. In a preclinical study, twenty C57BL/6 mice with footpad cancer and thirty-three rabbits with VX2 lung cancer were used. These animals received an intravenous injection of ICG at 0.5, 1, 2, or 5 mg/kg, and the cancers were detected using a fluorescent imaging system after 3, 6, 12, and 24 h. In a clinical study, fifty-one patients diagnosed with lung cancer and scheduled to undergo surgery were included. Fluorescent images of lung cancer were obtained, and the fluorescent signal was quantified. Based on a preclinical study, the optimal injection method for lung cancer detection was 2 mg/kg ICG 12 h before surgery. Among the 51 patients, ICG successfully detected 37 of 39 cases with a consolidation-to-tumor (C/T) ratio of >50% (TNR: 3.3 ± 1.2), while it failed in 12 cases with a C/T ratio ≤ 50% and 2 cases with anthracosis. ICG injection at 2 mg/kg, 12 h before surgery was optimal for lung cancer detection. Lung cancers with the C/T ratio > 50% were successfully detected using ICG with a detection rate of 95%, but not with the C/T ratio ≤ 50%. Therefore, further research is needed to develop fluorescent agents targeting lung cancer.

Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma.

OBJECTIVE: To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT). METHODS: Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram. RESULTS: The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P = 0.029 OR = 4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR = 1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model. CONCLUSION: The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography.

BACKGROUND: There is no consensus on 3-dimensional (3D) quantification method for solid component within part-solid nodules (PSNs). This study aimed to find the optimal attenuation threshold for the 3D solid component proportion in low-dose computed tomography (LDCT), namely the consolidation/tumor ratio of volume (CTRV), basing on its correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas (PAs) according to the 5th edition of World Health Organization classification. Then we tested the ability of CTRV to predict high-risk nonmucinous PAs in PSNs, and compare its performance with 2-dimensional (2D) measures and semantic features. METHODS: A total of 313 consecutive patients with 326 PSNs, who underwent LDCT within one month before surgery and were pathologically diagnosed with nonmucinous PAs, were retrospectively enrolled and were divided into training and testing cohorts according to scanners. The CTRV were automatically generated by setting a series of attenuation thresholds from - 400 to 50 HU with an interval of 50 HU. The Spearman's correlation was used to evaluate the correlation between the malignant grade of nonmucinous PAs and semantic, 2D, and 3D features in the training cohort. The semantic, 2D, and 3D models to predict high-risk nonmucinous PAs were constructed using multivariable logistic regression and validated in the testing cohort. The diagnostic performance of these models was evaluated by the area under curve (AUC) of receiver operating characteristic curve. RESULTS: The CTRV at attenuation threshold of -250 HU (CTRV- 250HU) showed the highest correlation coefficient among all attenuation thresholds (r = 0.655, P < 0.001), which was significantly higher than semantic, 2D, and other 3D features (all P < 0.001). The AUCs of CTRV- 250HU to predict high-risk nonmucinous PAs were 0.890 (0.843-0.927) in the training cohort and 0.832 (0.737-0.904) in the testing cohort, which outperformed 2D and semantic models (all P < 0.05). CONCLUSIONS: The optimal attenuation threshold was - 250 HU for solid component volumetry in LDCT, and the derived CTRV- 250HU might be valuable for the risk stratification and management of PSNs in lung cancer screening.

Comparison of radiological and pathological tumor sizes in resected non-small cell lung cancer.

OBJECTIVES: In non-small cell lung cancer (NSCLC), T factor plays an important role in determining staging. The present study aimed to determine the validity of preoperative evaluation of clinical T (cT) factor by comparing radiological and pathological tumor sizes. METHODS: Data for 1,799 patients with primary NSCLC who underwent curative surgery were investigated. The concordance between cT and pathological T (pT) factors was analyzed. Furthermore, we compared groups with an increase or decrease of ≥ 20% and groups with an increase or decrease of < 20% in the size change between preoperative radiological and pathological diameters. RESULTS: The mean sizes of the radiological solid components and the pathological invasive tumors were 1.90 cm and 1.99 cm, respectively, correlation degree = 0.782. The group with increased pathological invasive tumor size (≥ 20%) compared with the radiologic solid component was significantly more likely female, consolidation tumor ratio (CTR) ≤ 0.5, and within cT1. Multivariate logistic analysis identified CTR < 1, cT ≤ T1, and adenocarcinoma as independent risk factors for increased pT factor. CONCLUSION: The radiological invasive area of tumors with cT1, CTR < 1, or adenocarcinoma on preoperative CT may be underestimated compared with pathological invasive diameter.

[Prediction of Lymph Node Metastasis of Mixed Ground-glass Nodules 
Based on Clinical Imaging Information].

BACKGROUND: Previous studies have shown that lymph node metastasis only occurs in some mixed ground-glass nodules (mGGNs) which the pathological results were invasive adenocarcinoma (IAC). However, the presence of lymph node metastasis leads to the upgrading of tumor-node-metastasis (TNM) stage and worse prognosis of the patients, so it is important to perform the necessary evaluation before surgery to guide the operation method of lymph node. The aim of this study was to find suitable clinical and radiological indicators to distinguish whether mGGNs with pathology as IAC is accompanied by lymph node metastasis, and to construct a prediction model for lymph node metastasis. METHODS: From January 2014 to October 2019, the patients with resected IAC appearing as mGGNs in computed tomography (CT) scan were reviewed. All the lesions were divided into two groups (with lymph node metastasis or not) according to their lymph node status. Lasso regression model analysis by applying R software was used to evaluate the relationship between clinical and radiological parameters and lymph node metastasis of mGGNs. RESULTS: A total of 883 mGGNs patients were enroled in this study, among which, 12 (1.36%) showed lymph node metastasis. Lasso regression model analysis of clinical imaging information in mGGNs with lymph node metastasis showed that previous history of malignancy, mean density, mean density of solid components, burr sign and percentage of solid components were informative. Prediction model for lymph node metastasis in mGGNs was developed based on the results of Lasso regression model with area under curve=0.899. CONCLUSIONS: Clinical information combined with CT imaging information can predict lymph node metastasis in mGGNs.

Clinical, imaging and biochemical presentation of cystic pheochromocytomas.

OBJECTIVE: Cystic adrenal mass is a rare imaging presentation of pheochromocytoma. We aimed to describe the clinical, biochemical and imaging characteristics of patients with cystic pheochromocytoma. DESIGN: Single-centre, retrospective study, 2000-2020. PATIENTS: Consecutive patients with cystic pheochromocytoma were identified from our institutional pathology and adrenal tumour database. RESULTS: Of the 638 patients with pheochromocytomas, 21 (3.2%) had cystic pheochromocytomas (median age: 57 years, 57% women). Most pheochromocytomas were discovered incidentally (57%) or due to symptoms of catecholamine excess (24%). The median tumour size was 6.4 cm. On imaging, cystic pheochromocytomas were round or oval (90%), heterogeneous lesions (86%) with a thick solid rim (median rim thickness 13.9 mm, unenhanced computed tomography (CT) attenuation 40 Hounsfield units (HU), venous-phase CT attenuation 83 HU), and a median cystic component of 40% (unenhanced CT attenuation 17.6 HU, venous-phase CT attenuation 20.4 HU), and rarely with calcifications (15%). All 20 patients with biochemical testing had functioning tumours (adrenergic in 80%, noradrenergic in 20%). Total urinary metanephrine excretion correlated with the volume of the solid component (R2 = .75, p < .0001) but not the cystic component (R2 = .04, p = .4386). All patients underwent adrenalectomy (48% laparoscopic, 52% open), and the median duration of hospital stay was 4 days. CONCLUSIONS: Cystic pheochromocytomas are rare, large tumours with a phenotypic appearance that can masquerade as other adrenal cystic lesions. The degree of biochemical abnormality in cystic pheochromocytomas is associated with the volume of the solid component. All patients with adrenal cysts that have a solid component or an unenhanced attenuation >10 HU should undergo biochemical testing for pheochromocytoma.

Prediction of Lymph Node Metastasis of Mixed Ground-glass Nodules Based on Clinical Imaging Information / 中国肺癌杂志

BACKGROUND@#Previous studies have shown that lymph node metastasis only occurs in some mixed ground-glass nodules (mGGNs) which the pathological results were invasive adenocarcinoma (IAC). However, the presence of lymph node metastasis leads to the upgrading of tumor-node-metastasis (TNM) stage and worse prognosis of the patients, so it is important to perform the necessary evaluation before surgery to guide the operation method of lymph node. The aim of this study was to find suitable clinical and radiological indicators to distinguish whether mGGNs with pathology as IAC is accompanied by lymph node metastasis, and to construct a prediction model for lymph node metastasis.@*METHODS@#From January 2014 to October 2019, the patients with resected IAC appearing as mGGNs in computed tomography (CT) scan were reviewed. All the lesions were divided into two groups (with lymph node metastasis or not) according to their lymph node status. Lasso regression model analysis by applying R software was used to evaluate the relationship between clinical and radiological parameters and lymph node metastasis of mGGNs.@*RESULTS@#A total of 883 mGGNs patients were enroled in this study, among which, 12 (1.36%) showed lymph node metastasis. Lasso regression model analysis of clinical imaging information in mGGNs with lymph node metastasis showed that previous history of malignancy, mean density, mean density of solid components, burr sign and percentage of solid components were informative. Prediction model for lymph node metastasis in mGGNs was developed based on the results of Lasso regression model with area under curve=0.899.@*CONCLUSIONS@#Clinical information combined with CT imaging information can predict lymph node metastasis in mGGNs.

Mixed epithelial and stromal tumor of the kidney composed mainly of solid components: A case report.

Mixed epithelial and stromal tumor (MEST) is a relatively rare lesion of mixed epithelial and mesenchymal origin, consisting of epithelial components that form cysts and stromal cells that are positive for estrogen and progesterone receptors. The present case was a 54-year-old female who presented with hematuria. Abdominal ultrasonography revealed a 41 x 30 mm tumor in the right kidney, with the tumor protruding outward in the direction of the renal pelvis. Dynamic contrast-enhanced computed tomography and magnetic resonance imaging confirmed a solid tumor in the right kidney that showed gradual contrast enhancement and contained a central non-enhancing area with the appearance of a cystic component. Based on the imaging findings, the provisional diagnosis was papillary renal cell carcinoma or angiomyolipoma with epithelial cysts. Right nephrectomy was performed and the tumor was confirmed histopathologically as MEST. We report a very rare case of MEST that was composed mainly of solid components.

Impact of minimal solid and micropapillary components on invasive lung adenocarcinoma recurrence.

BACKGROUND: The specific impacts of solid and micropapillary components on prognosis in lung adenocarcinoma remain unclear. Herein, we elucidated their distinct contributions to lung adenocarcinoma recurrence. MATERIALS AND METHODS: Lung adenocarcinoma was classified into solid and micropapillary absent (S-M-); solid absent, micropapillary present (S-M+); micropapillary absent, solid present (S + M-); and solid and micropapillary present (S + M+). Cumulative incidence of recurrence (CIR) was calculated using competing risk analysis. RESULTS: Of 994 adenocarcinomas, 650 (65.4%) were classified as S-M-; 152 (15.3%), S-M+; 148 (14.9%), S + M-; and 44 (4.4%), S + M+. In total, 168 (16.9%) patients had recurrence; 16 (1.6%) died from other causes. S-M- had significantly lower CIR than other groups (S-M- vs. S-M+: P < 0.001, S-M- vs. S + M-: P < 0.001, S-M- vs. S + M+: P < 0.001); S + M- had significantly higher CIR than S-M+ (P = 0.002). These differences remained significant in multivariable analysis. In stage IA, S-M- had significantly lower CIR than other groups (S-M- vs. S-M+: P = 0.006, S-M- vs. S + M-: P < 0.001, S-M- vs. S + M+: P < 0.001); S + M- and S + M+ had significantly higher CIR than S-M+ (P = 0.005, P = 0.008, respectively). These differences remained significant in multivariable analysis. CIR was not significantly different between S + M- and S-M+ subgroups. CONCLUSIONS: The presence of solid or micropapillary component (≥1%) was an independent risk factor for CIR; patients with solid component alone had a higher CIR than those with micropapillary component alone. In IA lung adenocarcinoma, patients with both solid and micropapillary components had a higher CIR than those with micropapillary component alone; the proportion of solid or micropapillary component was not associated with CIR.

[Research Progress in 3D-reconstruction Based Imaging Analysis 
in Partial Solid Pulmonary Nodule].

The incidence and mortality of lung cancer rank first among all malignant tumors in China. With the popularization of high resolution computed tomography (CT) in clinic, chest CT has become an important means of clinical screening for early lung cancer and reducing the mortality of lung cancer. Imaging findings of early lung adenocarcinoma often show partial solid nodules with ground glass components. With the development of imaging, the relationship between the imaging features of some solid nodules and their prognosis has attracted more and more attention. At the same time, with the development of 3D-reconstruction technology, clinicians can improve the accuracy of diagnosis and treatment of such nodules.This article focuses on the traditional imaging analysis of partial solid nodules and the imaging analysis based on 3D reconstruction, and systematically expounds the advantages and disadvantages of both.
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Benign and malignant pulmonary part-solid nodules: differentiation via thin-section computed tomography.

BACKGROUND: Pulmonary part-solid nodules (PSNs) reportedly have a high possibility of malignancy, while benign PSNs are common. This study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section computed tomography (CT) features. METHODS: Patients with PSNs confirmed by postoperative pathological examination or follow-up (at the same period) were retrospectively enrolled from March 2016 to January 2020. The clinical data of patients and CT features of benign and malignant PSNs were reviewed and compared. Binary logistic regression analysis was performed to reveal the predictors of malignant PSNs. RESULTS: A total of 119 PSNs in 117 patients [age (mean ± standard deviation), 56±11 years; 70 women] were evaluated. Of the 119 PSNs, 44 (37.0%) were benign, and 75 (63.0%) were malignant (12 adenocarcinomas in situ, 22 minimally invasive adenocarcinomas, and 41 invasive adenocarcinomas). There were significant differences in the patients' age and smoking history between benign and malignant PSNs. In terms of CT characteristics, malignant and benign lesions significantly differed in the following CT features: whole nodule, internal solid component, and peripheral ground-glass opacity. The binary logistic regression analysis revealed that well-defined border [odds ratio (OR), 4.574; 95% confidence interval (CI), 1.186-17.643; P=0.027] and lobulation (OR, 61.739; 95% CI, 5.230-728.860; P=0.001) of the nodule, as well as irregular shape (OR, 9.502; 95% CI, 1.788-50.482; P=0.008) and scattered distribution (OR, 13.238; 95% CI, 1.359-128.924; P=0.026) of the internal solid components were significant independent predictors distinguishing malignant PSNs. However, the lesion shape, density, and margin were similar between malignant and benign lesions. CONCLUSIONS: Well-defined and lobulated PSNs with irregular and scattered solid components are highly likely to be malignant.

Research Progress in 3D-reconstruction Based Imaging Analysis in Partial Solid Pulmonary Nodule / 中国肺癌杂志

The incidence and mortality of lung cancer rank first among all malignant tumors in China. With the popularization of high resolution computed tomography (CT) in clinic, chest CT has become an important means of clinical screening for early lung cancer and reducing the mortality of lung cancer. Imaging findings of early lung adenocarcinoma often show partial solid nodules with ground glass components. With the development of imaging, the relationship between the imaging features of some solid nodules and their prognosis has attracted more and more attention. At the same time, with the development of 3D-reconstruction technology, clinicians can improve the accuracy of diagnosis and treatment of such nodules.This article focuses on the traditional imaging analysis of partial solid nodules and the imaging analysis based on 3D reconstruction, and systematically expounds the advantages and disadvantages of both.
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T1 invasive lung adenocarcinoma: Thin-section CT solid score and histological periostin expression predict tumor recurrence.

Adenocarcinoma is the most common histological type of non-small cell lung cancer (NSCLC), and various biomarkers for predicting its prognosis after surgical resection have been suggested, particularly in early-stage lung adenocarcinoma. Periostin (also referred to as POSTN, PN or osteoblast-specific factor) is an extracellular matrix protein, the expression of which is associated with tumor invasiveness in patients with NSCLC. In the present study, the novel approach, in which the thin-section CT findings prior to surgical resection and periostin expression of resected specimens were analyzed in combination, was undertaken to assess whether the findings could be a biomarker for predicting the outcomes following resection of T1 invasive lung adenocarcinoma. A total of 73 patients who underwent surgical resection between January 2000 and December 2009 were enrolled. A total of seven parameters were assessed in the thin-section CT scans: i) Contour; ii) part-solid ground-glass nodule or solid nodule; iii) percentage of solid component (the CT solid score); iv) presence of air-bronchogram and/or bubble-like lucencies; v) number of involved vessels; vi) shape linear strands between the nodule and the visceral pleura; and vii) number of linear strands between the nodule and the visceral pleura. Two chest radiologists independently assessed the parameters. Periostin expression was evaluated on the basis of the strength and extent of staining. Univariate and multivariate analyses were subsequently performed using the Cox proportional hazards model. There was a substantial to almost perfect agreement between the two observers with regard to classification of the seven thin-section CT parameters (κ=0.64-0.85). In the univariate analysis, a CT solid score >80%, pathological lymphatic invasion, tumor and lymph node status and high periostin expression were significantly associated with recurrence (all P<0.05). Multivariate analysis demonstrated that a CT solid score >80% and high periostin expression were risk factors for recurrence (P=0.002 and P=0.011, respectively). The cumulative recurrence rates among the three groups (both negative, CT solid score >80% or high periostin expression, or both positive) were significantly different (log-rank test, P<0.001). Although the solid component is already known to be a major predictor of outcome in lung adenocarcinomas according to previous studies, the combined analysis of CT solid score and periostin expression might predict the likelihood of tumor recurrence more precisely.

Identification of High-Risk of Recurrence in Clinical Stage I Non-Small Cell Lung Cancer.

OBJECTIVE: This study aimed to identify patients at a high risk of recurrence using preoperative high-resolution computed tomography (HRCT) in clinical stage I non-small cell lung cancer (NSCLC). METHODS: A total of 567 patients who underwent screening and 1,216 who underwent external validation for clinical stage I NSCLC underwent lobectomy or segmentectomy. Staging was used on the basis of the 8th edition of the tumor-node-metastasis classification. Recurrence-free survival (RFS) was estimated using the Kaplan-Meier method, and the multivariable Cox proportional hazards model was used to identify independent prognostic factors for RFS. RESULTS: A multivariable Cox analysis identified solid component size (hazard ratio [HR], 1.66; 95% confidence interval [CI] 1.30-2.12; P < 0.001) and pure solid type (HR, 1.82; 95% CI 1.11-2.96; P = 0.017) on HRCT findings as independent prognostic factors for RFS. When patients were divided into high-risk (n = 331; solid component size of >2 cm or pure solid type) and low-risk (n = 236; solid component size of ≤2 cm and part solid type) groups, there was a significant difference in RFS (HR, 5.33; 95% CI 3.09-9.19; 5-year RFS, 69.8% vs. 92.9%, respectively; P < 0.001). This was confirmed in the validation set (HR, 5.32; 95% CI 3.61-7.85; 5-year RFS, 72.0% vs. 94.8%, respectively; P < 0.001). CONCLUSIONS: In clinical stage I NSCLC, patients with a solid component size of >2 cm or pure solid type on HRCT were at a high risk of recurrence.