Dataset Information

Classification of Progression Patterns in Glioblastoma: Analysis of Predictive Factors and Clinical Implications.

ABSTRACT:

Background

This study was designed to explore the progression patterns of IDH-wildtype glioblastoma (GBM) at first recurrence after chemoradiotherapy.

Methods

Records from 247 patients who underwent progression after diagnosis of IDH-wildtype GBM was retrospectively reviewed. Progression patterns were classified as either local, distant, subependymal or leptomeningeal dissemination based on the preoperative and serial postoperative radiographic images. The clinical and molecular characteristics of different progression patterns were analyzed.

Results

A total of 186 (75.3%) patients had local progression, 15 (6.1%) patients had distant progression, 33 (13.3%) patients had subependymal dissemination, and 13 (5.3%) patients had leptomeningeal dissemination. The most favorable survival occurred in patients with local progression, while no significant difference of survival was found among patients with distant progression, subependymal or leptomeningeal dissemination who were thereby reclassified into non-local group. Multivariable analysis showed that chemotherapy was a protective factor for non-local progression, while gender of male, subventricular zone (SVZ) involvement and O⁶-methylguanine-DNA-methyltransferase (MGMT) promoter methylation were confirmed as risk factors for non-local progression (P < 0.05). Based on the factors screened by multivariable analysis, a nomogram was constructed which conferred high accuracy in predicting non-local progression. Patients in non-local group could be divided into long- and short-term survivors who differed in the rates of SVZ involvement, MGMT promoter methylation and reirradiation (P < 0.05), and a nomogram integrating these factors showed high accuracy in predicting long-term survivors.

Conclusion

Patients harboring different progression patterns conferred distinct clinical and molecular characteristics. Our nomograms could provide theoretical references for physicians to make more personalized and precise treatment decisions.

SUBMITTER: Jiang H

PROVIDER: S-EPMC7673412 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Classification of Progression Patterns in Glioblastoma: Analysis of Predictive Factors and Clinical Implications.

Jiang Haihui H Yu Kefu K Li Mingxiao M Cui Yong Y Ren Xiaohui X Yang Chuanwei C Zhao Xuzhe X Lin Song S

Frontiers in oncology 20201103

<h4>Background</h4>This study was designed to explore the progression patterns of IDH-wildtype glioblastoma (GBM) at first recurrence after chemoradiotherapy.<h4>Methods</h4>Records from 247 patients who underwent progression after diagnosis of IDH-wildtype GBM was retrospectively reviewed. Progression patterns were classified as either local, distant, subependymal or leptomeningeal dissemination based on the preoperative and serial postoperative radiographic images. The clinical and molecular c ...[more]

PMID: 33251147

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Project description:Background and Objectives: The "interstitial pneumonia with autoimmune features" (IPAF) criteria have been criticized because of the exclusion of usual interstitial pneumonia (UIP) patients with a single clinical or serological feature. To classify these patients, the term UIPAF was proposed. This study aims to describe clinical characteristics and predictive factors for progression of a cohort of interstitial lung disease (ILD) patients with at least one feature of autoimmunity, applying criteria for IPAF, specific connective tissue diseases (CTD), and a definition of UIPAF when possible. Methods: We retrospectively evaluated data on 133 consecutive patients with ILD at onset associated with at least one feature of autoimmunity, referred by pulmonologists to rheumatologists from March 2009 to March 2020. Patients received 33 (16.5-69.5) months of follow-up. Results: Among the 101 ILD patients included, 37 were diagnosed with IPAF, 53 with ILD-onset CTD, and 11 with UIPAF. IPAF patients had a lower prevalence of UIP pattern compared to CTD-ILD and UIPAF patients (10.8% vs. 32.1% vs. 100%, p < 0.01). During the follow-up, 4 IPAF (10.8%) and 2 UIPAF (18.2%) patients evolved into CTD-ILD. IPAF patients presented features not included in IPAF criteria, such as sicca syndrome (8.1%), and were more frequently affected by systemic hypertension (p < 0.01). Over one year, ILD progression (greater extent of fibrosis on HRCT and/or decline in PFTs) was less frequent in the IPAF group compared to CTD-ILD and UIPAF (32.3% vs. 58.8% vs. 72.7, p = 0.02). A UIP pattern and an IPAF predicted a faster (OR: 3.80, p = 0.01) and a slower (OR: 0.28, p = 0.02) ILD progression, respectively. Conclusions: IPAF criteria help identify patients who might develop a CTD-ILD, even though a single clinical or serological feature is respected. Future revisions of IPAF criteria should include sicca syndrome and separate UIP-pattern into a different definition (UIPAF), given its association with a different prognosis, independently from ILD classification.

Dataset Information

Classification of Progression Patterns in Glioblastoma: Analysis of Predictive Factors and Clinical Implications.

Background

Methods

Results

Conclusion

Publications

Classification of Progression Patterns in Glioblastoma: Analysis of Predictive Factors and Clinical Implications.

Similar Datasets

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