CT-texture analysis of subsolid nodules for differentiating invasive from in-situ and minimally invasive lung adenocarcinoma subtypes

Cohen, Julien G.; Reymond, Eric D.; Medici, Maud; Léderlin, Mathieu; Lantuéjoul, Sylvie; Laurent, François; Toffart, Anne‐Claire; Moreau–Gaudry, Alexandre; Jankowski, A.; Ferretti, G.

doi:10.1016/j.diii.2017.12.013

Cited by 21 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although there was no statistically difference in kurtosis and entropy, the values of them gradually increased as tumor malignant degree increased (from pNET to PDAC). This variation trend was in accordance with previous researches . Hence, this study could provide reference value for further large sample research and contribute to the development of CTTA.…”

Section: Discussionsupporting

confidence: 91%

See 1 more Smart Citation

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Liang

et al. 2018

Cancer Medicine

View full text Add to dashboard Cite

BackgroundTo explore the application value of computed tomography (CT) texture analysis in differentiating atypical pancreatic neuroendocrine tumors (pNET) from pancreatic ductal adenocarcinomas (PDAC).Materials and methodsThis single‐center retrospective study was approved by local institutional review board, and the requirement for informed consent was waived. We retrospectively analyzed 127 patients with 50 PDACs and 77 pNETs in pathology database between January 2012 and May 2017.These patients successfully finished preoperative contrast‐enhanced CT test. Texture parameters (mean, median, 5th, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis and entropy) were extracted from portal images and compared between PDAC and 77 pNET groups using proper statistical method. The optimal parameters for differentiating PDACs and atypical pNETs were gained through receiver operating characteristic (ROC) curves.ResultsOn the basis of arterial enhancement, 52 pNETs (67%, 52/77) were typical hypervascular and 25 pNETs (32%, 25/77) were atypical hypovascular. Compared with PDACs, atypical pNETs had statistically higher mean, median, 5th, 10th, and 25th percentiles (P = 0.006, 0.024, 0.000, 0.001, 0.021, respectively) and statistically lower skewness (P = 0.017). However, there were no difference for 75th, 90th percentiles, kurtosis and entropy between these two tumors (P = 0.232, 0.415, 0.143, 0.291, respectively). For differentiating PDACs and atypical pNETs, 5th percentile and 5th+skewness were optimal parameters for alone and combined diagnosis, respectively.ConclusionVolumetric CT texture features, especially combined diagnosis of 5th+skewness can be used as a quantitative tool to distinguish atypical pNETs from PDACs.

show abstract

Section: Discussionsupporting

confidence: 91%

“…This variation trend was in accordance with previous researches. 26,27 Hence, this study could provide reference value for further large sample research and contribute to the development of CTTA. This study had several limitations.…”

Section: T a B L E 5 Comparison Of Area Undermentioning

confidence: 86%

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Liang

et al. 2018

Cancer Medicine

View full text Add to dashboard Cite

show abstract

“…Son et al [ 24 ] demonstrated that the 75th percentile CT attenuation value could be used to distinguish IA from pre-invasive lesions. Kurtosis, skewness, or entropy of GGNs could be used for differentiating pre-invasive lesions and IA [ 24 – 26 ]. In addition, advanced radiomic features including shape and morphology metrics, Renyi dimensions, geometrical measures, and the gray-level co-occurrence matrix or the gray-level run length matrix might be useful for differential diagnosis of GGNs [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules

et al. 2018

View full text Add to dashboard Cite

ObjectivesTo evaluate and compare the diagnostic accuracy of high versus low attenuation thresholds for determining the solid component of ground-glass opacity nodules (GGNs) for the differential diagnosis of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA).MethodsEighty-six pathologically confirmed GGNs < 3 cm observed in 86 patients (27 male, 59 female; mean age, 59.3 ± 11.0 years) between January 2013 and December 2015 were retrospectively included. The solid component of each GGN was defined using two different attenuation thresholds: high (-160 Hounsfield units [HU]) and low (-400 HU). According to the presence or absence of solid portions, each GGN was categorized as a pure GGN or part-solid GGN. Solid components were regarded as indicators of invasive foci, suggesting MIA or IA.ResultsAmong the 86 GGNs, there were 57 cases of IA, 19 of MIA, and 10 of AIS. Using the high attenuation threshold, 44 were categorized as pure GGNs and 42 as part-solid GGNs. Using the low attenuation threshold, 13 were categorized as pure GGNs and 73 as part-solid GGNs. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for the invasive focus were 55.2%, 100%, 100%, 22.7%, and 60.4%, respectively, for the high attenuation threshold, and 93.4%, 80%, 97.2%, 61.5%, and 91.8%, respectively, for the low attenuation threshold.ConclusionThe low attenuation threshold was better than the conventional high attenuation threshold for determining the solid components of GGNs, which indicate invasive foci.

show abstract

“…Based on 70 adenocarcinoma patients, the proposed model achieved an accuracy of 86.3% in predicting ve histological subtypes of adenocarcinomas [9] . Then, 44 lung adenocarcinoma patients were studied by Cohen, which can differentiate AIS and MIA from IAC among subsolid lung nodules by computed tomography [10] . In addition, the segmentation techniques of computed tomographic three-dimensional (3D) indicated that computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma [11] .…”

Section: Backgoundmentioning

confidence: 99%

Control Study between CT Signs and Pathological Subtypes and Differentiation Degree in Lung Adenocarcinoma

Duan¹,

Shan²,

Bo³

et al. 2019

Preprint

View full text Add to dashboard Cite

Background: To study the relationship between CT signs and pathological subtypes and differentiation degree in lung adenocarcinoma.Methods: We retrospectively reviewed 394 cases’ CT images of lung adenocarcinoma and compared the diversity of images among PIL, MIA and IAC, as well as differentiation degrees of IAC by Kruskal-Wall test and X2 test.Results: There was no statistical difference of CT sign between PIL and MIA (P＞0.05). There are statistical differences among size and density of PIL, MIA and IAC (P＜0.01); the incidences of pleura traction sign, spicule sign, lobulation sign, tumor vascular sign, bronchial cut-off sign, air bronchogram sign and cavity sign in IAC were higher than the other two groups (P＜0.05); there was no statistical difference of vacuole sign among PIL, MIA and IAC (P＞0.05). There are statistical differences among size and density of three differentiated subgroups (P＜0.01); the incidences of pleura traction sign, spicule sign, tumor vascular sign, bronchial cut-off sign and cavity sign in poorly differentiated subgroup were highest (P＜0.01); the incidence of air bronchogram sign in highly differentiated subgroup was higher than moderately and poorly differentiated subgroup while incidence of lobulation sign was the lowest in three subgroups above (P＜0.05) ; there was no statistical difference of vacuole sign among three differentiated subgroups (P＞0.05).Conclusions: CT signs of lung adenocarcinoma are closely related to pathological subtypes and differentiation degrees，which would have great value to help predict tumor types and make treatment plans in clinical.

show abstract

CT-texture analysis of subsolid nodules for differentiating invasive from in-situ and minimally invasive lung adenocarcinoma subtypes

Abstract: CTTA has the potential to differentiate AIS and MIA from IVA among SSLNs. However, our results require further validation on a larger cohort.

Cited by 21 publications

References 31 publications

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules

Control Study between CT Signs and Pathological Subtypes and Differentiation Degree in Lung Adenocarcinoma

Contact Info

Product

Resources

About