Analysis of giant thoracic neoplasms: Correlations between imaging, pathology and surgical management.

Abstract:

BACKGROUND:A giant thoracic neoplasm is extremely rare and poorly understood. Our systemic study introduced computed tomography angiography (CTA) with three-dimensional (3D) reconstruction imaging and evaluated correlations between imaging, pathology, and surgical management. METHODS:Data from 45 patients undergoing surgery for giant thoracic neoplasm in our institution between May 2007 and November 2015 were collected. The clinical characteristics, imaging manifestations, preoperative biopsy, surgical management, postoperative pathology, and prognosis and their correlation were analyzed. RESULTS:The clinical characteristics, imaging manifestations, and pathological types were complicated. Four patients underwent CTA with 3D reconstruction imaging and feeding vessels were found in three cases. Twenty-four selected patients accepted preoperative biopsy, eight of which were inconsistent with postoperative pathology. Complete resection was performed in 39 cases, 20 of which underwent extended excision. The median survival duration of all patients was 58 months (range 3.0-118.0). The one, three, and five-year survival rates were 86.0%, 64.4%, and 47.0%, respectively. Univariate analyses showed tumor size and resection status were prognostic factors for survival (P = 0.003 and P < 0.001, respectively). CONCLUSIONS:A giant thoracic neoplasm should preferably be treated in experienced centers for precise diagnosis and optimal therapy schemes with comprehensive consideration of clinical characters, imaging manifestations, pathology, surgical management, and prognosis. Innovative CTA with 3D reconstruction imaging together with preoperative biopsy are feasible and effective in therapeutic decision-making and surgical planning. Complete surgical resection remains the mainstay of curative therapy for all resectable tumors.

journal_name

Thorac Cancer

journal_title

Thoracic cancer

authors

Feng Z,Li M,Liu F,Peng Y,Ren W,Xie H,Peng Z

doi

10.1111/1759-7714.12448

subject

Has Abstract

pub_date

2017-09-01 00:00:00

pages

402-409

issue

5

eissn

1759-7706

issn

1759-7714

journal_volume

8

pub_type

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