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Clinicopathological and immunohistochemical features of lung invasive mucinous adenocarcinoma based on computed tomography findings.

ABSTRACT: We performed an analysis to clarify differences in clinicopathological and molecular features of lung invasive mucinous adenocarcinoma (IMA) based on computed tomography (CT) findings and their impact on prognosis.On the basis of CT findings, we divided lung IMA into three subtypes: solid, bubbling, and pneumonic. We then investigated differences in clinicopathological characteristics, prognosis, and the expressions of well-identified biomarkers, including cyclooxygenase-2 (Cox-2), excision repair cross-complementation group 1 (ERCC1), ribonucleotide reductase M1 (RRM1), class III beta-tubulin, thymidylate synthase (TS), secreted protein acidic and rich in cysteine (SPARC), programmed cell death-1 ligand-1 (PD-L1), and epidermal growth factor receptor mutation, among the three subtypes.A total of 29 patients with resected lung IMA were analyzed. Compared with the solid or bubbling type, the pneumonic type had a higher proportion of symptoms, a larger tumor size, a higher pathological stage, and a significantly worse prognosis. The immunohistochemical findings tended to show high expression of RRM1, class III beta-tubulin, and Cox-2 in the tumor and of SPARC in the stroma, but not of ERCC1, TS, and PD-L1 in the tumor. None of the biomarkers with high expression levels in the tumor were prognostic biomarkers, but the expression of SPARC in the stroma was correlated with a poor outcome.Clinical and pathological features, in conjunction with molecular data, indicate that IMA should be divided into different subgroups. In our results, the pneumonic type was correlated with a significantly worse outcome. Further studies should be performed to confirm our conclusion and to explore its molecular implications.

SUBMITTER: Shimizu K

PROVIDER: S-EPMC5207454 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Clinicopathological and immunohistochemical features of lung invasive mucinous adenocarcinoma based on computed tomography findings.

Shimizu Katsuhiko K Okita Riki R Saisho Shinsuke S Maeda Ai A Nojima Yuji Y Nakata Masao M

OncoTargets and therapy 20161228

<h4>Background</h4>We performed an analysis to clarify differences in clinicopathological and molecular features of lung invasive mucinous adenocarcinoma (IMA) based on computed tomography (CT) findings and their impact on prognosis.<h4>Patients and methods</h4>On the basis of CT findings, we divided lung IMA into three subtypes: solid, bubbling, and pneumonic. We then investigated differences in clinicopathological characteristics, prognosis, and the expressions of well-identified biomarkers, i ...[more]

PMID: 28096683

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Project description:Objective: This study aimed to develop and validate predictive models using clinical parameters, radiomic features, and a combination of both for invasive mucinous adenocarcinoma (IMA) of the lung in patients with lung adenocarcinoma. Method: A total of 173 and 391 patients with IMA and non-IMA, respectively, were retrospectively analyzed from January 2017 to September 2022 in our hospital. Propensity Score Matching was used to match the 2 groups of patients. A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into training and test groups at a ratio of 7:3. The least absolute shrinkage and selection operator algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (logistic), support vector machine (SVM), and decision tree. The best-performing model was adopted, and the radiomics score (Radscore) was then computed. A clinical model was developed using logistic regression. Finally, a combined model was established based on a clinical model and a radiomics model. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis were used to evaluate the predictive value of the developed models. Results: Both clinical and radiomics models established using the logistic method showed the best performance. The Delong test revealed that the combined model was superior to the clinical and radiomics models (P = .018 and .020, respectively). The ROC-AUC (also decision curve analysis) of the combined model was 0.840 and 0.850 in the training and testing groups, respectively, which showed good predictive performance for IMA. The Brier scores for the combined model were 0.161 and 0.154 in the training and testing groups, respectively. Conclusion: The combined model incorporating radiomic CT features and clinical predictors may have the potential to predict IMA in patients with lung cancer.

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Dataset Information

Clinicopathological and immunohistochemical features of lung invasive mucinous adenocarcinoma based on computed tomography findings.

Publications

Clinicopathological and immunohistochemical features of lung invasive mucinous adenocarcinoma based on computed tomography findings.

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