Correlation in histological subtypes with high resolution computed tomography signatures of early stage lung adenocarcinoma

Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features

BACKGROUND

To build prediction models with radiomics features, clinical/conventional radiographic signs and combined scores for the discrimination of micropapillary or solid subtypes (high-risk subtypes) of lung adenocarcinoma.

METHODS

This retrospective study enrolled 351 patients with and without high-risk subtypes. Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation was performed to determine the optimal features of radiomics model. Missing clinical data were imputed by Multiple Imputation with Chain Equations (MICE). Clinical model with radiographic signs was built and scores of both models were integrated to establish combined model. Receiver operating characteristics (ROC) curves, area under ROC curves and decision curve analysis (DCA) were plotted to evaluate the model performance and clinical application.

RESULTS

Stratified splitting allocated 246 patients into training set. MICE for missing values obtained complete and unbiased data for the following analysis. Ninety radiomic features and four clinical/conventional radiographic signs were used to predict the high-risk subtypes. The radiomic model, clinical model and combined model achieved AUCs of 0.863 (95%CI: 0.817-0.909), 0.771 (95%CI: 0.713-0.713) and 0.872 (95%CI: 0.829-0.916) in the training set, and 0.849 (95%CI: 0.774-0.924), 0.778 (95%CI: 0.687-0.868) and 0.853 (95%CI: 0.782-0.925) in the test set. Decision curve showed that the radiomic and combined models were more clinically useful when the threshold reached 37.5%.

CONCLUSIONS

Radiomics features could facilitate the prediction of subtypes of lung adenocarcinoma. A simple combination of radiomics and clinical scores generated a robust model with high performance for the discrimination of micropapillary or solid subtype of lung adenocarcinoma.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma

RATIONALE AND OBJECTIVES

The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading.

MATERIALS AND METHODS

This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness.

RESULTS

Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram.

CONCLUSION

Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.

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.

Association of CT findings with invasive subtypes and the new grading system of lung adenocarcinoma

AIM

To predict the differentiation between invasive growth patterns and new grades of lung adenocarcinoma (LAC) using computed tomography (CT).

MATERIALS AND METHODS

The CT features of 180 surgically treated LAC patients were compared retrospectively to pathological invasive subtypes and tumour grades as defined by the new grading system published in 2021 by the World Health Organization. Two radiologists reviewed the images semi-quantitatively and independently. Univariable and multivariable regression models were built from the statistical means of their assessments to predict invasive subtypes and grades. The area under the curve (AUC) calculation was used to select the best models. The Youden index was applied to determine the cut-off values for radiological parameters.

RESULTS

The acinar/papillary patterns were associated with ill-defined margins, lower consolidation/tumour ratio and air bronchogram. The solid growth pattern was associated with a well-defined margin and hypodensity, and the micropapillary (MP) subtype with spiculation. From Grades 1 to 3, the amount of air bronchogram decreased and the consolidation/tumour ratio increased. In the sub-analyses, the best model for differentiating Grade 2 from Grade 1 had the following CT features: solid/subsolid type, consolidation/tumour ratio, well-defined margin, and air bronchogram (AUC = 0.783) and Grade 3 from Grade 2: size of the consolidation part/whole tumour ratio, size of the consolidation part, and well-defined margin (AUC = 0.759). The interobserver agreements between the two radiologists varied between 0.67 and 0.98.

CONCLUSIONS

Air bronchogram, consolidation/tumour ratio, and well-defined margin are among the best imaging findings to discriminate between both invasive subtypes and the new grades in LAC.

An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening

OBJECTIVES

To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists.

METHODS

A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy.

RESULTS

The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05).

CONCLUSIONS

The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert.

KEY POINTS

• Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.

Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma

PURPOSE

Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists.

MATERIALS AND METHODS

A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity.

RESULTS

No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05).

CONCLUSIONS

The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.

Radiomics for identifying lung adenocarcinomas with predominant lepidic growth manifesting as large pure ground-glass nodules on CT images

Purpose

To explore the value of radiomics in the identification of lung adenocarcinomas with predominant lepidic growth in pure ground-glass nodules (pGGNs) larger than 10 mm.

Methods

We retrospectively analyzed CT images of 204 patients with large pGGNs (≥ 10 mm) pathologically diagnosed as minimally invasive adenocarcinomas (MIAs), lepidic predominant adenocarcinomas (LPAs), and non-lepidic predominant adenocarcinomas (NLPAs). All pGGNs in the two groups (MIA/LPA and NLPA) were randomly divided into training and test cohorts. Forty-seven patients from another center formed the external validation cohort. Baseline features, including clinical data and CT morphological and quantitative parameters, were collected to establish a baseline model. The radiomics model was built with the optimal radiomics features. The combined model was developed using the rad_score and independent baseline predictors. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. The differential diagnosis performance of the models was compared with three radiologists (with 20+, 10+, and 3 years of experience) in the test cohort.

Results

The radiomics (training AUC: 0.833; test AUC: 0.804; and external validation AUC: 0.792) and combined (AUC: 0.849, 0.820, and 0.775, respectively) models performed better for discriminating than the baseline model (AUC: 0.756, 0.762, and 0.725, respectively) developed by tumor location and mean CT value of the whole nodule. The DeLong test showed that the AUCs of the combined and radiomics models were significantly increased in the training cohort. The highest AUC value of the radiologists was 0.600.

Conclusion

The application of CT radiomics improved the identification performance of lung adenocarcinomas with predominant lepidic growth appearing as pGGNs larger than 10 mm.

Predictors of Invasiveness in Adenocarcinoma of Lung with Lepidic Growth Pattern

Lung adenocarcinoma with lepidic growth pattern (LPA) is characterized by tumor cell proliferation along intact alveolar walls, and further classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive lepidic predominant adenocarcinoma (iLPA). Accurate diagnosis of lepidic lesions is critical for appropriate prognostication and management as five-year survival in patients with iLPA is lower than in those with AIS and MIA. We aimed to evaluate the accuracy of CT-guided core needle lung biopsy classifying LPA lesions and identify clinical and radiologic predictors of invasive disease in biopsied lesions. Thirty-four cases of adenocarcinoma with non-invasive lepidic growth pattern on core biopsy pathology that subsequently were resected between 2011 and 2018 were identified. Invasive LPA vs. non-invasive LPA (AIS or MIA) was defined based on explant pathology. Histopathology of core biopsy and resected tumor specimens was compared for concordance, and clinical, radiologic and pathologic variables were analyzed to assess for correlation with invasive disease. The majority of explanted tumors (70.6%) revealed invasive disease. Asian race (p = 0.03), history of extrathoracic malignancy (p = 0.02) and absence of smoking history (p = 0.03) were associated with invasive disease. CT-measured tumor size was not associated with invasiveness (p = 0.15). CT appearance of density (p = 0.61), shape (p = 0.78), and margin (p = 0.24) did not demonstrate a significant difference between the two subgroups. Invasiveness of tumors with lepidic growth patterns can be underestimated on transthoracic core needle biopsies. Asian race, absence of smoking, and history of extrathoracic malignancy were associated with invasive disease.

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.

Verification of the eighth edition of the UICC-TNM classification on surgically resected lung adenocarcinoma: Comparison with previous classification in a local center

BACKGROUND

The UICC 8th TNM classification of lung cancer has been changed dramatically, especially in measuring methods of T-desriptors. Different from squamous- or small-cell carcinomas, in which the solid- and the invasive-diameter mostly agree with each other, the diameter of the radiological solid part and that of pathological invasive part in adenocarcinomas often does not match.

AIM

We aimed to determine radiological and pathological tumor diameters of pulmonary adenocarcinomas with clinicopathological factors and evaluate the validity of the 8th edition in comparison with the 7th edition.

METHODS AND RESULTS

We retrospectively analyzed clinicopathological factors of 429 patients with surgically resected pulmonary adenocarcinomas. The maximum tumor and their solid-part diameters were measured using thin-sectioned computed tomography and compared with pathological tumor and invasive diameters. Overall survival (OS) rate was determined using the Kaplan-Meier method for different subgroups of clinicopathological factors. Akaike's information criteria (AIC) was used as a discriminative measure for the univariate Cox model for the 7th and 8th editions. Multivariate Cox regression analysis was performed to explore independent prognostic factors. Correlation coefficients between radiological and pathological diameters in the 7th and 8th editions were 0.911 and 0.888, respectively, without a significant difference. The major reasons for the difference in the 8th edition were the presence of intratumoral fibrosis and papillary growth pattern. The weighted kappa coefficients in the 8th edition were superior those in the 7th edition for both the T and Stage classifications. In the univariate Cox model, AIC levels were the lowest in the 8th edition. Multivariate analysis revealed that age, lymphovascular invasion, pT(8th), and stage were the most important determinants for OS.

CONCLUSION

The UICC 8th edition is a more discriminative classification than the 7th edition. For subsolid nodules, continuous efforts are necessary to increase the universality of the measurement of solid and invasive diameters.

Discriminating Small-Sized (2 cm or Less), Noncalcified, Solitary Pulmonary Tuberculoma and Solid Lung Adenocarcinoma in Tuberculosis-Endemic Areas

Background. Pulmonary tuberculoma can mimic lung malignancy and thereby pose a diagnostic dilemma to clinicians. The purpose of this study was to establish an accurate, convenient, and clinically practical model for distinguishing small-sized, noncalcified, solitary pulmonary tuberculoma from solid lung adenocarcinoma. Methods. Thirty-one patients with noncalcified, solitary tuberculoma and 30 patients with solid adenocarcinoma were enrolled. Clinical characteristics and CT morphological features of lesions were compared between the two groups. Multivariate logistic regression analyses were applied to identify independent predictors of pulmonary tuberculoma and lung adenocarcinoma. Receiver operating characteristic (ROC) analysis was performed to investigate the discriminating efficacy. Results. The mean age of patients with tuberculoma and adenocarcinoma was 46.8 ± 12.3 years (range, 28–64) and 61.1 ± 9.9 years (range, 41–77), respectively. No significant differences were observed concerning smoking history and smoking index, underlying disease, or tumor markers between the two groups. Univariate and multivariate analyses showed age and lobulation combined with pleural indentation demonstrated excellent discrimination. The sensitivity, specificity, accuracy, and the area under the ROC curve were 87.1%, 93.3%, 90.2%, and 0.956 (95% confidence interval (CI), 0.901–1.000), respectively. Conclusion. The combination of clinical characteristics and CT morphological features can be used to distinguish noncalcified, solitary tuberculoma from solid adenocarcinoma with high diagnostic performance and has a clinical application value.

Genomic landscape of ground glass opacities (GGOs) in East Asians

Background

Understanding the genomic landscape of early-stage lung adenocarcinoma (LUAD) may provide new insights into the molecular evolution in the early stages of LUAD.

Methods

Through sequencing of 79 spatially distinct regions from 37 patients with ground glass opacities (GGOs), we provided a comprehensive mutational landscape of GGOs, highlighting the importance of ancestry differences.

Results

Our study had several interesting features. First, epidermal growth factor receptor (EGFR), BRAF (v-RAF murine sarcoma viral oncogene homologue B1), and ERBB2 (Erb-B2 Receptor Tyrosine Kinase 2, also known as HER2) were more frequently mutated in our study, which supports the notion that EGFR is considered to be a major driver and tends to drive the occurrence of LUAD. Second, Signature 1, Signature 3, and Signature 6 were identified in patients with GGOs. Our results further suggested that Signature 1 was more prominent among early mutations. Third, compared with LUADs, GGOs exhibited significantly lower levers of arm-level copy number variation (CNV)—which alter the diploid status of DNA, and lower focal CNVs.

Conclusions

In our study, 79 samples of patients were included to analyze the GGO gene profile, revealing the genetic heterogeneity of GGO in East Asian population, and providing guidance for prognosis analysis of GGO patients by comparison with LUAD. Our study revealed that GGOs had fewer genomic alterations and simpler genomic profiles than LUADs. The most commonly altered processes were related to the receptor tyrosine kinase (RTK)/Ras/phosphatidylinositol-3-kinase (PI3K) signaling pathways in GGOs, and EGFR alterations were the dominant genetic changes across all targetable somatic changes.

Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT

OBJECTIVES

To develop a model for differentiating the predominant subtype-based prognostic groups of lung adenocarcinoma using CT radiomic features, and to validate its performance in comparison with radiologists' assessments.

METHODS

A total of 993 patients presenting with invasive lung adenocarcinoma between March 2010 and June 2016 were identified. Predominant histologic subtypes were categorized into three groups according to their prognosis (group 0: lepidic; group 1: acinar/papillary; group 2: solid/micropapillary). Seven hundred eighteen radiomic features were extracted from segmented lung cancers on contrast-enhanced CT. A model-development set was formed from the images of 893 patients, while 100 image sets were reserved for testing. A least absolute shrinkage and selection operator method was used for feature selection. Performance of the radiomic model was evaluated using receiver operating characteristic curve analysis, and accuracy on the test set was compared with that of three radiologists with varying experiences (6, 7, and 19 years in chest CT).

RESULTS

Our model differentiated the three groups with areas under the curve (AUCs) of 0.892 and 0.895 on the development and test sets, respectively. In pairwise discrimination, the AUC was highest for group 0 vs. 2 (0.984). The accuracy of the model on the test set was higher than the averaged accuracy of the three radiologists without statistical significance (73.0% vs. 61.7%, p = 0.059). For group 2, the model achieved higher PPV than the observers (85.7% vs. 35.0-48.4%).

CONCLUSIONS

Predominant subtype-based prognostic groups of lung adenocarcinoma were classified by a CT-based radiomic model with comparable performance to radiologists.

KEY POINTS

• A CT-based radiomic model differentiated three prognosis-based subtype groups of lung adenocarcinoma with areas under the curve (AUCs) of 0.892 and 0.895 on development and test sets, respectively. • The CT-based radiomic model showed near perfect discrimination between group 0 and group 2 (AUCs, 0.984-1.000). • The accuracy of the CT-based radiomic model was comparable to the averaged accuracy of the three radiologists with 6, 7, and 19 years of clinical experience in chest CT (73.0% vs. 61.7%, p = 0.059), achieving a higher positive predictive value for group 2 than the observers (85.7% vs. 35.0-48.4%).

Volume Doubling Times of Lung Adenocarcinomas: Correlation with Predominant Histologic Subtypes and Prognosis

Background The volume doubling time (VDT) is a key parameter in the differentiation of aggressive tumors from slow-growing tumors. How different histologic subtypes of primary lung adenocarcinomas vary in their VDT and the prognostic value of this measurement is unknown. Purpose To investigate differences in VDT between the predominant histologic subtypes of primary lung adenocarcinomas and to assess the correlation between VDT and prognosis. Materials and Methods This retrospective study included patients who underwent at least two serial CT examinations before undergoing operation between July 2010 and December 2018. Three-dimensional tumor segmentation was performed on two CT images and VDTs were calculated. VDTs were compared between predominant histologic subtypes and lesion types by using Kruskal-Wallis tests. Disease-free survival (DFS) was obtained in patients undergoing surgical procedures before July 2017. Univariable and multivariable Cox proportional hazards regression analyses were performed to determine predictors of DFS. Results Among 268 patients (mean age, 64 years ± 8 [standard deviation]; 143 men), there were 30 lepidic, 87 acinar, 109 papillary, and 42 solid or micropapillary predominant subtypes. The median VDT was 529 days (interquartile range, 278-872 days) for lung adenocarcinomas. VDTs differed across subtypes (P < .001) and were shortest in solid or micropapillary subtypes (229 days; interquartile range, 77-530 days). Solid lesions (VDT, 248 days) had shorter VDTs than subsolid lesions (part-solid lesions, 665 days; nonsolid lesions, 648 days) (P < .001). In the 148 patients (mean age, 64 years ± 8; 89 men) included in the survival analysis, 35 patients had disease recurrence and 17 patients died. VDT (<400 days) was an independent risk factor for poor DFS (hazard ratio, 2.6; P = .01) and higher TNM stage. Adding VDT to TNM stage improved model performance (C-index, 0.69 for TNM stage vs 0.77 for combined VDT class and TNM stage; P = .002). Conclusion Volume doubling times varied significantly according to the predominant histologic subtypes of lung adenocarcinoma and had additional prognostic value for disease-free survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Ko in this issue.

Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (≤ 3 cm): PET/CT findings and correlations with lymph node metastasis

OBJECTIVE

To investigate the PET/CT findings in lung invasive adenocarcinoma with minor components of micropapillary or solid contents and its association with lymph node metastasis.

MATERIALS AND METHODS

A total of 506 lung invasive adenocarcinoma (≤ 3 cm) patients who underwent a PET/CT examination and resection surgery were included. According to the proportion of solid/micropapillary components, the patients were classified into three groups: solid/micropapillary-negative (SMPN) (n = 258), solid/micropapillary-minor (SMPM; > 5% not predominant) (n = 158) and solid/micropapillary-predominant (SMPP; > 5% most dominant) (n = 90). The patients' PET/CT findings, including SUVmax, MTV, TLG and CT characteristics, and other clinical factors were compared by one-way ANOVA test. Logistic regression analysis was done to identify the most predictive findings for lymph node metastasis.

RESULTS

The value of SUVmax, MTV, TLG and tumor size was highest in SMPP group, followed by SMPM and SMPN group (P < 0.001).The areas under the curve for SUVmax, MTV and TLG for node metastasis were 0.822, 0.843 and 0.835, respectively. Univariate analysis found that the SMPP and SMPM group had more lymph node metastasis than the SMPN group (P < 0.001). Furthermore, the lymph node metastasis group had higher CEA, SUVmax, MTV, TLG, tumor size and more pleural invasion (P < 0.001). Logistic regression analysis found that SMPP pathological type, SMPM pathological type, higher CEA and male patients were risk factors for lymph node metastasis (P < 0.01).

CONCLUSIONS

Lung invasive adenocarcinoma with micropapillary or solid contents had higher SUVmax, MTV, TLG and tumor size and was associated with lymph node metastasis, even if they were not predominant.

Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest

Background

The purpose of this study was to develop a predictive model that could accurately predict the malignancy of the pulmonary ground-glass nodules (GGNs) and the invasiveness of the malignant GGNs.

Methods

The authors built two binary classification models that could predict the malignancy of the pulmonary GGNs and the invasiveness of the malignant GGNs.

Results

Results of our developed model showed random forest could achieve 95.1% accuracy to predict the malignancy of GGNs and 83.0% accuracy to predict the invasiveness of the malignant GGNs.

Conclusions

The malignancy and invasiveness of pulmonary GGNs could be predicted by random forest.