Radiographic and pathological analysis of small lung adenocarcinoma using the new IASLC classification

Honda, Takeshi; Kondo, Tetsuro; Murakami, Shuji; Saito, Haruhiro; Oshita, Fumihiro; Ito, Hiroyuki; Tsuboi, Masahiro; Nakayama, Haruhiko; Yokose, Tomoyuki; Konishi, Yoichi; Isobe, Takeshi; Yamada, Kozo

doi:10.1016/j.crad.2012.09.002

Cited by 49 publications

(42 citation statements)

References 19 publications

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“…Tumors with high concavity are often an indicator of poorly differentiated adenocarcinoma and outcomes 40–42 . Our observations were thus consistent with the previous studies that tumors with irregular and non-spherical shape are more aggressive, and thus are poor prognostic indicators 12, 43, 44 .…”

Section: Discussionsupporting

confidence: 93%

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Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Yip

Liu²,

Parmar

et al. 2017

Sci Rep

100

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Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined “semantic” and computer-derived “radiomic” features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32–41 radiomic features were associated with the binary semantic features (AUC = 0.56–0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen’s correlation| = 0.002–0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.

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Section: Discussionsupporting

confidence: 93%

“…Cavitation is often observed in rapidly growing tumors as they can outgrow the blood supply resulting in air-filled cavities 8, 53, 54 . Tumors with air bronchogram contain tube-like structures and are highly metastatic 40, 44 . Cavities and tube-like structures give tumors heterogeneous appearance (Fig.…”

Section: Discussionmentioning

confidence: 99%

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Yip

Liu²,

Parmar

et al. 2017

Sci Rep

100

View full text Add to dashboard Cite

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“…A growing number of papers in the radiology literature where ground glass vs solid opacity corresponds to lepidic vs invasive histology as well as the pathology literature support this concept. 43–45 …”

Section: Discussionmentioning

confidence: 99%

Prognostic Significance of Adenocarcinoma In Situ, Minimally Invasive Adenocarcinoma, and Nonmucinous Lepidic Predominant Invasive Adenocarcinoma of the Lung in Patients With Stage I Disease

Kadota

Villena-Vargas

Yoshizawa

et al. 2014

American Journal of Surgical Pathology

216

160

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According to the IASLC/ATS/ERS classification, the lepidic predominant pattern consists of 3 subtypes: adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and nonmucinous lepidic predominant invasive adenocarcinoma. We reviewed tumor slides from 1038 patients with stage I lung adenocarcinoma, recording the percentage of each histologic pattern and measuring invasive tumor size. Tumors were classified according to the IASLC/ATS/ERS classification: 2 were AIS, 34 MIA, and 103 lepidic predominant invasive. Cumulative incidence of recurrence (CIR) was used to estimate the probability of recurrence. Patients with AIS and MIA experienced no recurrences. Patients with lepidic predominant invasive tumors had a lower risk of recurrence (5-year CIR, 8%) than non-lepidic predominant tumors (n=899; 19%; P=0.003). Patients with >50% lepidic pattern tumors experienced no recurrences (n=84), those with >10%-

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“…Therefore, for the surgical resection of a tumor to be performed adequately, a precise pre-operative diagnosis may be critical for choosing the resection boundary of the tumor. Second, several studies have reported that pathologic invasive features are not rare, even in small and pure GGNs [6], [13]–[17]. Therefore, histologic prediction of GGNs by qualitative visual assessment alone may miss the pathologic invasive component of pure GGN adenocarcinomas.…”

Section: Discussionmentioning

confidence: 99%

Quantitative CT Analysis of Pulmonary Ground-Glass Opacity Nodules for the Distinction of Invasive Adenocarcinoma from Pre-Invasive or Minimally Invasive Adenocarcinoma

et al. 2014

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ObjectivesWe aimed to analyze the CT findings of ground-glass opacity nodules diagnosed pathologically as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma in order to investigate whether quantitative CT parameters enable distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.MethodsWe reviewed CT images and pathologic specimens from 191 resected ground-glass opacity nodules with little or no solid component at CT. Nodule size, volume, density, mass, skewness/kurtosis, and CT attenuation values at the 2.5th–97.5th percentiles on histogram, and texture parameters (uniformity and entropy) were assessed from CT datasets.ResultsOf 191 tumors, 38 were AISs (20%), 61 were MIAs (32%), and 92 (48%) were invasive adenocarcinomas. Multivariate logistic regression analysis helped identify the 75th percentile CT attenuation value (P = 0.04) and entropy (P<0.01) as independent predictors for invasive adenocarcinoma, with an area under the receiver operating characteristic curve of 0.780.ConclusionQuantitative analysis of preoperative CT imaging metrics can help distinguish invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

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Radiographic and pathological analysis of small lung adenocarcinoma using the new IASLC classification

Cited by 49 publications

References 19 publications

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Prognostic Significance of Adenocarcinoma In Situ, Minimally Invasive Adenocarcinoma, and Nonmucinous Lepidic Predominant Invasive Adenocarcinoma of the Lung in Patients With Stage I Disease

Quantitative CT Analysis of Pulmonary Ground-Glass Opacity Nodules for the Distinction of Invasive Adenocarcinoma from Pre-Invasive or Minimally Invasive Adenocarcinoma

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