Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.

BACKGROUND: In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival. MATERIALS AND METHODS: Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features. RESULTS: At multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance. CONCLUSION: Radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology. IMPLICATIONS FOR PRACTICE: Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.

Prognostic impact of nomogram based on whole tumour size, tumour disappearance ratio on CT and SUVmax on PET in lung adenocarcinoma.

OBJECTIVES: Lung adenocarcinoma frequently manifests as subsolid nodules, and the solid portion and ground-glass-opacity (GGO) portion on CT have different prognostic significance. Therefore, current T descriptor, defined as the whole tumour diameter without discrimination between solid and GGO, is insufficient. We aimed to determine the prognostic significance of solid tumour size and attempt to include prognostic factors such as tumour disappearance rate (TDR) on CT and SUVmax on PET/CT. METHODS: Five hundred and ninety-five patients with completely resected lung adenocarcinoma were analyzed. We developed a nomogram using whole tumour size, TDR, and SUVmax. External validation was performed in another 102 patients. RESULTS: In patients with tumours measuring ≤2 cm and >2 to 3 cm, disease free survival (DFS) was significantly associated with solid tumour size (P < 0.001), but not with whole tumour size (P = 0.052). Developed nomogram was significantly superior to the conventional T stage (area under the curve of survival ROC; P = 0.013 by net reclassification improvement) in stratification of patient survival. In the external validation group, significant difference was noted in DFS according to proposed T stage (P = 0.009). CONCLUSIONS: Nomogram-based T descriptors provide better prediction of survival and assessment of individual risks than conventional T descriptors. KEY POINTS: • Current measurement of whole tumour diameter including ground-glass opacity is insufficient • TDR enables differentiation between invasive solid portion and non-invasive GGO portion • SUVmax demonstrates the biological aggressiveness of the tumour • We developed a nomogram using whole tumour size, TDR, and SUVmax • Nomogram-based clinical T descriptors provide better prediction of survival.

Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.

OBJECTIVE: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). MATERIALS AND METHODS: B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared. RESULTS: When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8-92.5% vs. 82.1-93.1%; p < 0.001), accuracy (77.9-88.9% vs. 86.2-90.9%; p = 0.038), and positive predictive value (PPV) (60.2-83.3% vs. 70.4-85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3-88.8% vs. 86.3-95.0%; p = 0.120) and negative predictive value (91.4-93.5% vs. 92.9-97.3%; p = 0.259). CONCLUSION: Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.

Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma.

INTRODUCTION: Lung adenocarcinomas (ADCs) with a micropapillary pattern have been reported to have a poor prognosis. However, few studies have reported on the imaging-based identification of a micropapillary component, and all of them have been subjective studies dealing with qualitative computed tomography variables. We aimed to explore imaging phenotyping using a radiomics approach for predicting a micropapillary pattern within lung ADC. METHODS: We enrolled 339 patients who underwent complete resection for lung ADC. Histologic subtypes and grades of the ADC were classified. The amount of micropapillary component was determined. Clinical features and conventional imaging variables such as tumor disappearance rate and maximum standardized uptake value on positron emission tomography were assessed. Quantitative computed tomography analysis was performed on the basis of histogram, size and shape, Gray level co-occurrence matrix-based features, and intensity variance and size zone variance-based features. RESULTS: Higher tumor stage (OR = 3.270, 95% confidence interval [CI]: 1.483-7.212), intermediate grade (OR = 2.977, 95% CI: 1.066-8.316), lower value of the minimum of the whole pixel value (OR = 0.725, 95% CI: 0.527-0.98800), and lower value of the variance of the positive pixel value (OR = 0.961, 95% CI: 0.927-0.997) were identified as being predictive of a micropapillary component within lung ADC. On the other hand, maximum standardized uptake value and tumor disappearance rate were not significantly different in groups with a micropapillary pattern constituting at least 5% or less than 5% of the entire tumor. CONCLUSION: A radiomics approach can be used to interrogate an entire tumor in a noninvasive manner. Combining imaging parameters with clinical features can provide added diagnostic value to identify the presence of a micropapillary component and thus, can influence proper treatment planning.

Role of CT and PET Imaging in Predicting Tumor Recurrence and Survival in Patients with Lung Adenocarcinoma: A Comparison with the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification of Lung Adenocarcinoma.

INTRODUCTION: Recently, a new lung adenocarcinoma classification scheme was published. The prognostic value of this new classification has not been elaborated together with the value of imaging biomarkers including computed tomography (CT) and positron emission tomography (PET). METHODS: We reviewed pathologic specimens and imaging characteristics of primary tumors from 723 consecutive patients who underwent surgical resection for lung adenocarcinoma. On pathology, the predominant histologic subtype and pattern group were quantified. Tumor-shadow disappearance ratio (TDR) on CT and maximum standardized uptake value (SUVmax) on PET were assessed. The relationships between those variables and survival (overall survival [OS] and disease-free survival) were analyzed by using Kaplan-Meier curves and Cox regression analyses. RESULTS: The median follow-up period was 3.8 years. There were 137 patients (19%) with recurrence and 167 patients (23%) with metastasis after surgical resection. Among 723 patients, 35 patients (4.8%) had adenocarcinoma in situ, 34 patients (4.7%) had minimally invasive adenocarcinoma, 125 patients (17.3%) had lepidic predominant, 314 patients (43.4%) had acinar predominant, 65 patients (9.0%) had papillary predominant, 23 patients (3.2%) had micropapillary predominant, 113 patients (15.6%) had solid predominant, and 14 patients (1.9%) had variant adenocarcinomas. OS and disease-free survival rates were significantly different according to TDR on CT and SUVmax on PET, predominant subtypes, and pattern groups. On multivariate analysis, the SUVmax (p < 0.001), TDR (p = 0.038), and pattern group (p = 0.015) were independent predictors of OS. CONCLUSIONS: TDR on CT, SUVmax on PET, and the new histologic classification schemes appear to be promising parameters for the prognostic stratification of patients with lung adenocarcinomas, allowing for the triage of patients who necessitate further staging workup and adjuvant therapy.