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A clinical spectrum of resectable lung adenocarcinoma with micropapillary component (MPC) concurrently presenting as mixed ground-glass opacity nodules.
Zhu, Ziwen; Jiang, Weizhen; Zhou, Danhong; Zhu, Weidong; Chen, Cheng.
Afiliación
  • Zhu Z; Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Jiang W; Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Zhou D; Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Zhu W; Pathology Department, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Chen C; Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Cancer Biomark ; 2023 Nov 18.
Article en En | MEDLINE | ID: mdl-38143336
ABSTRACT

BACKGROUND:

In clinical practice, preoperative identification of mixed ground-glass opacity (mGGO) nodules with micropapillary component (MPC) to facilitate the implementation of individualized therapeutic strategies and avoid unnecessary surgery is increasingly important

OBJECTIVE:

This study aimed to build a predictive model based on clinical and radiological variables for the early identification of MPC in lung adenocarcinoma presenting as mGGO nodules.

METHODS:

The enrolled 741 lung adenocarcinoma patients were randomly divided into a training cohort and a validation cohort (31 ratio). The pathological specimens and preoperative images of malignant mGGO nodules from the study subjects were retrospectively reviewed. Furthermore, in the training cohort, selected clinical and radiological variables were utilized to construct a predictive model for MPC prediction.

RESULTS:

The MPC was found in 228 (43.3%) patients in the training cohort and 72 (41.1%) patients in the validation cohort. Based on the predictive nomogram, the air bronchogram was defined as the most dominant independent risk factor for MPC of mGGO nodules, followed by the maximum computed tomography (CT) value (> 200), adjacent to pleura, gender (male), and vacuolar sign. The nomogram demonstrated good discriminative ability with a C-index of 0.783 (95%[CI] 0.744-0.822) in the training cohort and a C-index of 0.799 (95%[CI] 0.732-0.866) in the validation cohort Additionally, by using the bootstrapping method, this predictive model calculated a corrected AUC of 0.774 (95% CI 0.770-0.779) in the training cohort.

CONCLUSIONS:

This study proposed a predictive model for preoperative identification of MPC in known lung adenocarcinomas presenting as mGGO nodules to facilitate individualized therapy. This nomogram model needs to be further externally validated by subsequent multicenter studies.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Cancer Biomark Asunto de la revista: BIOQUIMICA / NEOPLASIAS Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Cancer Biomark Asunto de la revista: BIOQUIMICA / NEOPLASIAS Año: 2023 Tipo del documento: Article País de afiliación: China