Exploring the Value of Features of Lung Texture in Distinguishing Between Usual and Nonspecific Interstitial Pneumonia.

RATIONALE AND OBJECTIVES: This article aims to explore the potential use of lung texture assessed in CT images in distinguishing between the usual interstitial pneumonia and the nonspecific interstitial pneumonia. MATERIALS AND METHODS: A retrospective analysis of 96 cases of interstitial pneumonia was performed. Among these cases, there were 40 cases of usual interstitial pneumonia (UIP) and 56 cases of the nonspecific interstitial pneumonia (NSIP) . All of the patients underwent computed tomography (CT) scans. A lung intelligence kit (LK) was utilized to perform lung segmentation and texture feature extraction. The significant variables were determined by variance analysis, least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Finally, a multivariate logistic regression model was established to distinguish between the two types of interstitial pneumonia. Receiver operating characteristic (ROC) curves, area under the curve (AUC) values, sensitivity, and specificity were used to evaluate the performance of the established model. RESULTS: A total of 100 texture features were extracted from the whole lung that was segmented by LK, and 8 features remained after feature reduction. The AUC, sensitivity, and specificity of the multivariate logistic regression model in the training group and the test group were 0.952 and 0.838, 0.821 and 0.667, and 0.949 and 0.824, respectively. CONCLUSION: It is possible to distinguish between UIP and NSIP using lung texture features obtained from CT images.

Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature.

OBJECTIVES: To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy. MATERIALS AND METHODS: LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). RESULTS: The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001). CONCLUSION: Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management. KEY POINTS: • The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.

Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.

BACKGROUND: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR-targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)-based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule. MATERIALS AND METHODS: A total of 467 eligible patients were divided into training and validation cohorts (n = 306 and 161, respectively). Radiomics features were extracted from unenhanced CT images by using Pyradiomics. A CT-based radiomics signature for distinguishing EGFR mutation status was constructed using the random forest (RF) method in the training cohort and then tested in the validation cohort. A combination of the radiomics signature with a clinical factors model was also constructed using the RF method. The performance of the model was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: In this study, 64.2% (300/467) of the patients showed EGFR mutations. L858R mutation of exon 21 was the most common mutation type (185/301). We identified a CT-based radiomics signature that successfully discriminated between EGFR positive and EGFR negative in the training cohort (AUC = 0.831) and the validation cohort (AUC = 0.789). The radiomics signature combined with the clinical factors model was not superior to the simple radiomics signature in the two cohorts (p > .05). CONCLUSION: As a noninvasive method, the CT-based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule. IMPLICATIONS FOR PRACTICE: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR-targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography-based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutation status of subsolid LADCs when mutational profiling is not available or possible.

CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma.

OBJECTIVES: Pulmonary granulomatous nodule (GN) with spiculated or lobulated appearance are indistinguishable from solid lung adenocarcinoma (SADC) based on CT morphological features, and partial false-positive findings on PET/CT. The objective of this study was to investigate the ability of quantitative CT radiomics for preoperatively differentiating solitary atypical GN from SADC. METHODS: 302 eligible patients (SADC = 209, GN = 93) were evaluated in this retrospective study and were divided into training (n = 211) and validation cohorts (n = 91). Radiomics features were extracted from plain and vein-phase CT images. The L1 regularized logistic regression model was used to identify the optimal radiomics features for construction of a radiomics model in differentiate solitary GN from SADC. The performance of the constructed radiomics model was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). RESULTS: 16.7% (35/209) of SADC were misdiagnosed as GN and 24.7% (23/93) of GN were misdiagnosed as lung cancer before surgery. The AUCs of combined radiomics and clinical risk factors were 0.935, 0.902, and 0.923 in the training cohort of plain radiomics(PR), vein radiomics, and plain and vein radiomics, and were 0.817, 0835, and 0.841 in the validation cohort of three models, respectively. PR combined with clinical risk factors (PRC) performed better than simple radiomics models (p < 0.05). The diagnostic accuracy of PRC in the total cohorts was similar to our radiologists (p ≥ 0.05). CONCLUSIONS: As a noninvasive method, PRC has the ability to identify SADC and GN with spiculation or lobulation.

Correlations between computed tomography and positron emission tomography/computed tomography findings and pathology in 6 cases of pulmonary epithelioid angiosarcoma.

Previous studies on primary pulmonary epithelioid angiosarcoma (PEA) have been mostly clinical or pathological case reports. We here summarize findings from computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) analyses of PEA to improve the diagnosis and differentiation of this rare tumor.We conducted a retrospective analysis of the clinical findings, radiological imaging, and pathological findings of 6 cases of primary PEA confirmed by surgery, biopsy, and pathology. All cases were evaluated by CT and x-ray prior to surgery, and 2 cases were further examined by PET/CT.CT images indicated maximum tumor diameters of 2.4 to 9.8 cm and inhomogeneous density, with 1 case exhibiting nodular calcification. Contrast-enhanced CT revealed inhomogeneous enhancement with visible necrosis in all 6 cases, while 3 cases had hilar and mediastinal lymph node metastasis. Five cases displayed extensive tumor involvement with extension into the chest wall, mild-to-moderate levels of pleural effusion, and varying degrees of volume loss in the corresponding hemithorax. One case had limited pleural thickening and invasion. Preoperative PET/CT of 1 case revealed abnormal fluorine-18 fluorodeoxyglucose (F-FDG) uptake by the tumor and multiple enlarged right hilar and mediastinal lymph nodes, right diffuse pleural thickening, and systemic multiple bone metastasis. In the other case, PET/CT scan at 7 months after surgery revealed pleural thickening and mediastinal lymph nodes with increased F-FDG uptake on the surgical side. Immunohistochemistry analyses determined that all 6 tumors were positive for CD34, CD31, ERG, and vimentin.CT and PET/CT findings reveal that malignant characteristics, including extensive pleural thickening, invasion and metastasis, and pleural effusion, are common in PEA. Imaging data are only supportive; therefore, the final diagnosis should be based on pathology and immunohistochemistry analyses.

Is a 5-mm diameter an appropriate cut-off value for the diagnosis of atypical adenomatous hyperplasia and adenocarcinoma in situ on chest computed tomography and pathological examination?

BACKGROUND: Preinvasive lesions, such as atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), usually appear as pure ground-glass nodules (pGGNs) on thin-section computed tomography (TSCT). AAH is usually less than 5 mm wide on imaging and pathological examinations. We aimed to determine whether a 5-mm cut-off value was appropriate for the diagnosis of AAH and AIS. METHODS: We retrospectively analyzed the performance of TSCT in evaluating 80 pathologically confirmed preinvasive lesions (33 AAH lesions in 31 patients and 47 AIS lesions in 45 patients). We compared the following characteristics between the AAH and AIS groups: lesion diameter, density, rim, lobulation, spiculation, vacuole sign, aerated bronchus sign, pleural indentation sign, and pathological findings. RESULTS: All 80 lesions appeared as pGGNs. On TSCT, the average diameter of AAH lesions (6.0±1.64 mm) was significantly smaller than that of AIS lesions (8.7±3.16 mm; P<0.001). The area under the curve (AUC) for diameter was 0.792, and the best diagnostic cut-off value was 6.99 mm. On gross pathological examination, the average diameter of AAH lesions (4.6±1.99 mm) was significantly smaller that of AIS lesions (6.8±2.06 mm; P<0.001). The AUC was 0.794, and the best diagnostic cut-off value was 4.5 mm. The vacuole sign was common in AIS (P=0.021). AAH did not significantly differ from AIS (P>0.05) in terms of average CT value, uniformity of density, morphology, rim, lobulation, spiculation, pleural indentation sign, and aerated bronchus sign. CONCLUSIONS: Lesion size and the vacuole sign were beneficial in the diagnosis of AAH and AIS. The vacuole sign was common in AIS. The best diagnostic cut-off value of nodular diameter for differentiating between AAH and AIS was 6.99 mm on TSCT and 4.5 mm on gross pathology.

Malignant solitary pulmonary nodules: assessment of mass growth rate and doubling time at follow-up CT.

BACKGROUND: The differentiation of benign and malignant solitary pulmonary nodules (SPNs), especially subsolid nodules, is still challenging because of the small size, slow growth, and atypical imaging characteristics of these nodules. We aimed to determine the significance of mass growth rate (MGR) and mass doubling time (MDT) at follow-up CT of malignant SPNs. METHODS: This retrospective study included 167 patients (169 SPNs, diameter 8-30 mm). Among the 169 SPNs, 114 malignant SPNs were classified into three types: pure ground-glass nodules (pGGNs), part-solid nodules (pSNs), and solid nodules (SNs). These patients were followed up for at least 3 months. Three-dimensional manual segmentation was performed for all these nodules, and the intra- and inter-observer variabilities of diameter, volume, and mass measurement were assessed. From initial and follow-up CT scans, growth rates of the diameter, volume, and mass of the SPNs were compared. MDT and volume doubling time (VDT) were calculated and were compared among groups. RESULTS: Mass measurements had the best inter-observer consistency and intra-observer repeatability; the coefficients of variation of the mass measurements were the smallest. The mean growth rates of the diameter, volume, and mass of pGGNs, pSNs, and SNs significantly differed at different time points (P<0.001). Mean MDTs and VDTs of pGGNs, pSNs, and SNs were 655 vs. 848 days, 462 vs. 598 days, and 230 vs. 267 days, respectively (P<0.05). CONCLUSIONS: Mass measurements are an objective and accurate indicator in SPN assessment. During a 2-year follow-up, the mean growth rates of the diameter, volume, and mass of pGGNs, pSNs, and SNs differed at different time points, the greatest difference was observed in mean MGR. Mean MDT of malignant SPNs is less than the mean VDT.

A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram.

BACKGROUND: Lymph node metastasis (LNM) of lung cancer is an important factor related to survival and recurrence. The association between radiomics features of lung cancer and LNM remains unclear. We developed and validated a radiomics nomogram to predict LNM in solid lung adenocarcinoma. METHODS: A total of 159 eligible patients with solid lung adenocarcinoma were divided into training (n=106) and validation cohorts (n=53). Radiomics features were extracted from venous-phase CT images. We built a radiomics nomogram using a multivariate logistic regression model combined with CT-reported lymph node (LN) status. The performance of the radiomics nomogram was evaluated using the area under curve (AUC) of receiver operating characteristic curve. We performed decision curve analysis (DCA) within training and validation cohorts to assess the clinical usefulness of the nomogram. RESULTS: Fourteen radiomics features were chosen from 94 candidate features to build a radiomics signature that significantly correlated with LNM. The model showed good calibration and discrimination in the training cohort, with an AUC of 0.871 (95% CI: 0.804-0.937), sensitivity of 85.71% and specificity of 77.19%. In the validation cohort, AUC was 0.856 (95% CI: 0.745-0.966), sensitivity was 91.66%, and specificity was 82.14%. DCA demonstrated that the nomogram was clinically useful. The nomogram also showed good predictive ability in patients at high risk for LNM in the CT-reported LN negative (cN0) subgroup. CONCLUSIONS: The radiomics nomogram, based on preoperative CT images, can be used as a noninvasive method to predict LNM in patients with solid lung adenocarcinoma.

Stage I synchronous multiple primary non-small cell lung cancer: CT findings and the effect of TNM staging with the 7th and 8th editions on prognosis.

BACKGROUND: Studies have reported that up to 8% of non-small cell lung cancers (NSCLC) involve multiple lesions; no detailed study has assessed the prognosis of early synchronous multiple primary non-small cell lung cancer (SMPNSCLC) (T1N0M0, T2aN0M0). We aimed to assess the spiral CT manifestations of SMPNSCLC during stage I and evaluate the effect of TNM staging with the 7th and 8th editions on the prognosis. METHODS: We retrospectively analyzed the data of patients who were examined, operated, and pathologically confirmed as having NSCLC from January 1, 2009, to December 31, 2010, and were followed-up for 5 years. The number of cases with stage I SMPNSCLC and solitary primary NSCLC (SPNSCLC) was 36 and 133 as per the 7th edition TNM staging system and 34 and 111 as per the 8th edition TNM staging system, respectively. The relationship between sex, age, smoking history, emphysema, surgical procedure, pathological type, tumor location, and tumor size was evaluated between the two groups, along with the correlation between prognosis and TNM staging with the 7th and 8th editions. RESULTS: A total of 1,948 cases of NSCLC underwent surgery, including 36 cases of stage I SMPNSCLC (77 lesions; 1.85%) with an age of onset of 44-86 years (median age, 60 years). The tumors primarily included adenocarcinoma (93.5%), with a diameter of 0.4-4.5 cm (median, 2.3 cm). CT indicated round/oval tumors in 81.8% cases, lobulation in 79.2% cases, spiculation sign in 70.1% cases, bronchial truncation sign in 31.2% cases, and pleural indentation in 75.3% cases. Moreover, CT indicated the presence of 36 (46.8%) solid nodules and 41 (53.2%) sub-solid nodules. With the 7th edition TNM staging system, the 5-year overall survival (OS) and disease-free survival (DFS) rates for stage ISMPNSCLC were 86.1% and 72.2%, respectively, which did not significantly differ from the prognosis of 133 cases of stage I SPNSCLC (P=0.587, P=0.273). With the 8th edition TNM staging system, the 5-year OS and DFS rates for stage I SMPNSCLC were 88.2% and 73.5%, respectively, which also did not significantly differ with the prognosis of 111 cases of stage I SPNSCLC (P=0.413, P=0.235). CONCLUSIONS: Adenocarcinoma was the main pathological type among the cases with stage I SMPNSCLC. Multiple synchronous lesions almost had the malignant characteristics of primary lung cancer, particularly the presence of single or multiple sub-solid nodules. Moreover, stage I SMPNSCLC has a similar prognosis as stage I SPNSCLC. The postoperative outcomes of stage I SMPNSCLC patients remained consistent regardless of whether the 7th or 8th edition TNM staging system was used for staging.