Radiologic Predictors for Clinical Stage IA Lung Adenocarcinoma with Ground Glass Components: A Multi-Center Study of Long-Term Outcomes

Can CT texture analysis parameters be used as imaging biomarkers for prediction of malignancy in canine splenic tumors?

Splenic hemangiosarcoma has morphological similarities to benign nodular hyperplasia. Computed tomography (CT) texture analysis can analyze the texture of images that the naive human eye cannot detect. Recently, there have been attempts to incorporate CT texture analysis with artificial intelligence in human medicine. This retrospective, analytical design study aimed to assess the feasibility of CT texture analysis in splenic masses and investigate predictive biomarkers of splenic hemangiosarcoma in dogs. Parameters for dogs with hemangiosarcoma and nodular hyperplasia were compared, and an independent parameter that could differentiate between them was selected. Discriminant analysis was performed to assess the ability to discriminate the two splenic masses and compare the relative importance of the parameters. A total of 23 dogs were sampled, including 16 splenic nodular hyperplasia and seven hemangiosarcoma. In each dog, total 38 radiomic parameters were extracted from first-, second-, and higher-order matrices. Thirteen parameters had significant differences between hemangiosarcoma and nodular hyperplasia. Skewness in the first-order matrix and GLRLM_LGRE and GLZLM_ZLNU in the second, higher-order matrix were determined as independent parameters. A discriminant equation consisting of skewness, GLZLM_LGZE, and GLZLM_ZLNU was derived, and the cross-validation verification result showed an accuracy of 95.7%. Skewness was the most influential parameter for the discrimination of the two masses. The study results supported using CT texture analysis to help differentiate hemangiosarcoma from nodular hyperplasia in dogs. This new diagnostic approach can be used for developing future machine learning-based texture analysis tools.

Assessment of Clinical Stage IA Lung Adenocarcinoma with pN1/N2 Metastasis Using CT Quantitative Texture Analysis

OBJECTIVE

To explore the application of texture analysis basing on computed tomography (CT) images in predicting lymph-node metastasis in patients with clinical stage IA lung adenocarcinoma.

METHODS

In total, 256 patients with clinical stage IA lung adenocarcinoma who had underwentgone preoperative CT examinations were enrolled. A total of 25 texture features using MaZda (version 4.6) software and conventional radiological features were extracted from raw CT data sets. Based on surgical results, patients were stratified into lymph node metastasis-positive and -negative groups. Independent-sample t-tests and Mann-Whitney U tests were used to compare continuous variables between the groups. Continuity-correction and χ2 tests were used for categorical variable comparison. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of lymph-node metastasis.

RESULTS

In total, 256 clinical stage IA lung adenocarcinoma cases were proved by pathology: 39 (15.23%) cases with lymph-node metastasis (14 N1a, seven N1b, six N2a1, ten N2a2, and two N2b) and 217 (84.77%) cases without lymph-node metastasis. Univariate and multivariate logistic regression analyses demonstrated that total volume (OR 3.777, p=0.015), average CT value of whole tumor (OR 16.271, p<0.001), three texture parameters (mean OR 8.473, p<0.001; skewness OR 6.393, p=0.001; and entropy OR 0.343, p=0.049) were independent factors associated with lymph-node status. As such, early-stage lung adenocarcinoma with higher total volume (>4.05 cm3), average CT value of whole tumor (>-70 HU), mean (>133.79), entropy (>1.98), and lower skewness (≤0.02) pointed to positive lymph-node metastasis.

CONCLUSION

Texture parameters were independent factors associated with lymph-node status in clinical stage IA lung adenocarcinoma.

Controversies on lung cancers manifesting as part-solid nodules

PURPOSE

Summarise survival of patients with resected lung cancers manifesting as part-solid nodules (PSNs).

METHODS

PubMed/MEDLINE and EMBASE databases were searched for all studies/clinical trials on CT-detected lung cancer in English before 21 December 2015 to identify surgically resected lung cancers manifesting as PSNs. Outcome measures were lung cancer-specific survival (LCS), overall survival (OS), or disease-free survival (DFS). All PSNs were classified by the percentage of solid component to the entire nodule diameter into category PSNs <80% or category PSNs ≥80%.

RESULTS

Twenty studies reported on PSNs <80%: 7 reported DFS and 2 OS of 100%, 6 DFS 96.3-98.7%, and 11 OS 94.7-98.9% (median DFS 100% and OS 97.5%). Twenty-seven studies reported on PSNs ≥80%: 1 DFS and 2 OS of 100%, 19 DFS 48.0%-98.0% (median 82.6%), and 16 reported OS 43.0%-98.0% (median DFS 82.6%, OS 85.5%). Both DFS and OS were always higher for PSNs <80%.

CONCLUSION

A clear definition of the upper limit of solid component of a PSN is needed to avoid misclassification because cell-types and outcomes are different for PSN and solid nodules. The workup should be based on the size of the solid component.

KEY POINTS

• Lung cancers manifesting as PSNs are slow growing with high cure rates. • Upper limits of the solid component are important for correct interpretation. • Consensus definition is important for the management of PSNs. • Median disease-free-survival (DFS) increased with decreasing size of the nodule.

Prognostic significance of histologic classification and tumor disappearance rate by computed tomography in lung cancer

Background

We investigated the prognostic value of the International Association for the Study of Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) classification and assessed the relationship between pathologic invasiveness and tumor disappearance rate (TDR) in lung adenocarcinoma with ground-glass opacity (GGO).

Methods

We reviewed data from 202 consecutive patients operated on between 2000 and 2009 for clinical T1-2N0 lung adenocarcinoma with GGO and reclassified their histologic subtypes according to the IASLC/ATS/ERS classification. Thirty-nine patients had adenocarcinoma in situ (AIS), 29 minimally invasive adenocarcinoma (MIA), 75 lepidic predominant invasive adenocarcinoma (LPA), and 59 non-lepidic predominant invasive adenocarcinoma (NLPA). Survival outcomes were compared according to histologic subtype and TDR.

Results

The mean age was 58 years and 101 patients (50%) were male. Lobectomy was performed in 161 patients (79.7%), wedge resection in 34 (16.8%), and segmentectomy in 7 (3.5%). Patients with AIS, MIA, and LPA had significantly smaller tumor sizes, earlier pathologic T stages, and lower incidences of lymphatic/pleural invasion than those with NLPA. The 5-year recurrence-free survival (RFS) rates were 95.1%, 94.5%, and 87.6% in the AIS + MIA, LPA, and NLPA groups, respectively (P=0.029). Tumors with a TDR>75% were associated with lepidic predominant histologic subtype and less pathologic invasiveness. The 5-year RFS rates were 97.4% in tumors with a TDR >75% and 87.8% in tumors with a TDR ≤75% (P=0.0009).

Conclusions

Histologic subtype according to the IASLC/ATS/ERS classification and TDR both correlated with pathologic invasiveness and predicted survival in patients with lung adenocarcinoma with GGO.

Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging?

Increased clinical use of chest high-resolution computed tomography results in increased identification of lung adenocarcinomas and persistent subsolid opacities. However, these lesions range from very indolent to extremely aggressive tumors. Clinically relevant diagnostic tools to noninvasively risk stratify and guide individualized management of these lesions are lacking. Research efforts investigating semiquantitative measures to decrease interrater and intrarater variability are emerging, and in some cases steps have been taken to automate this process. However, many such methods currently are still suboptimal, require validation and are not yet clinically applicable. The computer-aided nodule assessment and risk yield software application represents a validated tool for the automated, quantitative, and noninvasive tool for risk stratification of adenocarcinoma lung nodules. Computer-aided nodule assessment and risk yield correlates well with consensus histology and postsurgical patient outcomes, and therefore may help to guide individualized patient management, for example, in identification of nodules amenable to radiological surveillance, or in need of adjunctive therapy.