CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction

Abstract Background: Tyrosine kinase inhibitors (TKIs) provide clinical benefits to the lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, non-invasively determine EGFR mutation status in patients before targeted therapy remains a challenge. This study aimed to develop and validate a nomogram for preoperative prediction of EGFR mutation status in patients with lung adenocarcinoma.Methods: This study retrospectively collected medical records of 403 patients with histologically confirmed lung adenocarcinoma from January 2016 and June 2020. The patients were divided into development and validation cohorts. The preoperative information on all patients was obtained, including clinical characteristics and computed tomography (CT) features. Multivariate logistic regression analysis was used to develop the predictive model. We combined CT features and clinical risk factors and used them to build a prediction nomogram. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. The nomogram was further validated in an independent external cohort.Results: The predictive factors incorporated in the personalized prediction nomogram included smoking history (OR, 0.2; 95% CI: 0.1, 0.4; P < 0.001), bubble-like lucency (OR, 2.2; 95% CI: 1.3, 3.8; P = 0.003), pleural attachment (OR, 0.4; 95% CI: 0.2, 0.7, P = 0.001) and thickened adjacent bronchovascular bundles (OR, 3.1; 95% CI: 1.8, 5.3; P < 0.001). Based on these parameters, the prediction model has good discrimination and calibration ability. The area under the curve in the development and validation cohorts were 0.784 (95% CI: 0.733, 0.835) and 0.740 (95% CI: 0.643, 0.838), respectively. Decision curve analysis showed that the model was clinically useful.Conclusions: This study presented a nomogram that contained CT features and clinical risk factors, which could conveniently and non-invasively predict EGFR mutation status in patients with lung adenocarcinoma before surgery.

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Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.565755 ◽

2021 ◽

Vol 11 ◽

Author(s):

Lei-Lei Wu ◽

Jin-Long Wang ◽

Wei Huang ◽

Xuan Liu ◽

Yang-Yu Huang ◽

...

Keyword(s):

Computed Tomography ◽

Squamous Cell Carcinoma ◽

Quantitative Analysis ◽

High Risk ◽

Clinical Features ◽

Risk Groups ◽

Ct Images ◽

Prognostic Modeling ◽

Model C ◽

Ct Features

ObjectiveTo evaluate the effectiveness of a novel computerized quantitative analysis based on histopathological and computed tomography (CT) images for predicting the postoperative prognosis of esophageal squamous cell carcinoma (ESCC) patients.MethodsWe retrospectively reviewed the medical records of 153 ESCC patients who underwent esophagectomy alone and quantitatively analyzed digital histological specimens and diagnostic CT images. We cut pathological images (6000 × 6000) into 50 × 50 patches; each patient had 14,400 patches. Cluster analysis was used to process these patches. We used the pathological clusters to all patches ratio (PCPR) of each case for pathological features and we obtained 20 PCPR quantitative features. Totally, 125 computerized quantitative (20 PCPR and 105 CT) features were extracted. We used a recursive feature elimination approach to select features. A Cox hazard model with L1 penalization was used for prognostic indexing. We compared the following prognostic models: Model A: clinical features; Model B: quantitative CT and clinical features; Model C: quantitative histopathological and clinical features; and Model D: combined information of clinical, CT, and histopathology. Indices of concordance (C-index) and leave-one-out cross-validation (LOOCV) were used to assess prognostic model accuracy.ResultsFive PCPR and eight CT features were treated as significant indicators in ESCC prognosis. C-indices adjusted for LOOCV were comparable among four models, 0.596 (Model A) vs. 0.658 (Model B) vs. 0.651 (Model C), and improved to 0.711with Model D combining information of clinical, CT, and histopathology (all p<0.05). Using Model D, we stratified patients into low- and high-risk groups. The 3-year overall survival rates of low- and high-risk patients were 38.0% and 25.0%, respectively (p<0.001).ConclusionQuantitative prognostic modeling using a combination of clinical data, histopathological, and CT images can stratify ESCC patients with surgery alone into high-risk and low-risk groups.

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Radiomics analysis for pathological classification prediction in BI-RADS category 4 mammographic calcifications

The Breast ◽

10.1016/s0960-9776(19)30187-0 ◽

2019 ◽

Vol 44 ◽

pp. S43-S44

Author(s):

C. Lei ◽

W. Wei ◽

Z. Liu ◽

Q. Xiong ◽

C. Yang ◽

...

Keyword(s):

Pathological Classification ◽

Classification Prediction

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High-Resolution CT Features: Prognostic Significance in Peripheral Lung Adenocarcinoma with Bronchioloalveolar Carcinoma Components

Respiration ◽

10.1159/000068410 ◽

2003 ◽

Vol 70 (1) ◽

pp. 36-42 ◽

Cited By ~ 31

Author(s):

Shodayu Takashima ◽

Yuichiro Maruyama ◽

Minoru Hasegawa ◽

Akitoshi Saito ◽

Masayuki Haniuda ◽

...

Keyword(s):

High Resolution ◽

Lung Adenocarcinoma ◽

Prognostic Significance ◽

Bronchioloalveolar Carcinoma ◽

High Resolution Ct ◽

Peripheral Lung ◽

Ct Features

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CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma

Radiology ◽

10.1148/radiol.2016151455 ◽

2016 ◽

Vol 280 (1) ◽

pp. 271-280 ◽

Cited By ~ 77

Author(s):

Ying Liu ◽

Jongphil Kim ◽

Fangyuan Qu ◽

Shichang Liu ◽

Hua Wang ◽

...

Keyword(s):

Epidermal Growth Factor Receptor ◽

Growth Factor ◽

Epidermal Growth Factor ◽

Lung Adenocarcinoma ◽

Growth Factor Receptor ◽

Mutation Status ◽

Epidermal Growth ◽

Ct Features

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Predictors of CT Morphologic Features to Identify Spread Through Air Spaces Preoperatively in Small-Sized Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.548430 ◽

2021 ◽

Vol 10 ◽

Author(s):

Lin Qi ◽

Ke Xue ◽

Yongjun Cai ◽

Jinjuan Lu ◽

Xiaohu Li ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Roc Analysis ◽

Operating Characteristic ◽

Characteristic Curve ◽

Area Under The Curve ◽

Logistic Models ◽

Glass Ribbon ◽

Ground Glass ◽

Ct Features ◽

Spread Through Air Spaces

ObjectivesThis study aimed to explore the predictive CT features of spread through air spaces (STAS) in patients with small-sized lung adenocarcinoma.MethodsFrom January 2017 to May 2019, patients with confirmed pathology of small-sized lung adenocarcinoma (less than or equal to 2 cm) and who underwent surgery were retrospectively analyzed. The clinical, pathological, and surgical information and CT features were analyzed.ResultsA total of 47 patients with STAS (males, 61.7%; mean age, 56 ± 8years) and 143 patients without STAS (males, 58%; mean age, 53 ± 11 years) were included. Pathologically, papillary, micropapillary, solid predominant subtypes, and vascular and pleural invasion were most commonly observed features in the STAS group. Radiologically, higher consolidation tumor ratio (CTR), presence of spiculation, satellites, ground glass ribbon sign, pleural attachment, and unclear tumor–lung interface were more commonly observed features in the STAS group. CTR, presence of ground glass ribbons and pleural connection, and absence of cystic airspaces were considered as stable predictors of STAS in multivariate logistic models. The receiver operating characteristic curve (ROC) analysis for predicting STAS demonstrated higher area under the curve (AUC) in the model that used CTR (0.760, 95% confidence interval, 0.69–0.83) for predicting STAS than in the model that used long diameter of entire lesion (0.640).ConclusionsCTR is the best CT sign for predicting STAS in small-sized lung adenocarcinoma. The ground glass ribbon is a newly found indicator and has the potential for predicting STAS.

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