Publications by authors named "Qiugen Hu"

6 Publications

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Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer.

Breast 2021 Sep 11;60:90-97. Epub 2021 Sep 11.

Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China. Electronic address:

Background: One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance.

Objective: To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance.

Methods: Clinical data of non-metastatic breast cancer patients diagnosed between May 1, 2015 and December 31, 2018 and preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were retrospectively collected from three hospitals in China. The significant clinicopathological characteristics and radiomic signatures were included in multivariable logistic regression to establish a combined model to predict endocrine resistance in the training set, and validate the internal and external validation set.

Results: A total of 744 female non-metastatic breast cancer patients from three hospitals in China were included. In the training cohort, the AUC of the Radiomic-Clinical combined model to predict endocrine resistance was 0.975, which was higher than clinical model (0.849), IHC4 model (0.682) and similar as radiomic model (0.941). Also, the AUC of the combined model in the internal (0.921) and external validation cohort (0.955) were higher than clinical model and IHC4 model. The sensitivity of combined model was higher than radiomic alone, and got the best thresholding of the AUC.

Conclusion: This study developed and validated a pretreatment multiparametric MRI-based radiomic-clinical combined model and showed good performance in predicting endocrine resistance.
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http://dx.doi.org/10.1016/j.breast.2021.09.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449264PMC
September 2021

Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.

EBioMedicine 2021 Jul 4;69:103460. Epub 2021 Jul 4.

Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address:

Background: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer.

Methods: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558.

Findings: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.

Funding: No funding.
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http://dx.doi.org/10.1016/j.ebiom.2021.103460DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261009PMC
July 2021

Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics-Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer.

JAMA Netw Open 2020 12 1;3(12):e2028086. Epub 2020 Dec 1.

Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Centre, Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.

Importance: Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking.

Objective: To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer.

Design, Setting, And Participants: This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020.

Exposure: Clinical and DCE-MRI radiomic signatures.

Main Outcomes And Measures: The primary end points were ALNM and DFS.

Results: This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone.

Conclusions And Relevance: This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.28086DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724560PMC
December 2020

Secreted Frizzled-Related Protein 2 and Extracellular Volume Fraction in Patients with Heart Failure.

Oxid Med Cell Longev 2020 6;2020:2563508. Epub 2020 May 6.

Department of Cardiology, Shunde Hospital, Southern Medical University, Foshan, China.

Background: Quantification of extracellular volume (ECV) fraction by cardiovascular magnetic resonance (CMR) has emerged as a noninvasive diagnostic tool to assess myocardial fibrosis. Secreted frizzled-related protein 2 (SFRP2) appears to play an important role in cardiac fibrosis. We aimed to evaluate the association between SFRP2 and myocardial fibrosis and the prognostic value of ECV fraction in patients with heart failure (HF).

Methods: In this prospective cohort study, 72 hospitalized adult patients (age ≥ 18 years) with severe decompensated HF were included. CMR measurements and T1 mapping were performed to calculate ECV fraction. Serum SFRP2 level was detected by an enzyme-linked immunosorbent assay kit. All patients were followed up, and the primary outcomes were composite events including all-cause mortality and HF hospitalization.

Results: During the median follow-up of 12 months, 27 (37.5%) patients experienced primary outcome events and had higher levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP), SFRP2, and ECV fraction compared with those without events. In Pearson correlation analysis, levels of SFRP2 ( = 0.33), high-sensitivity C-reactive protein ( = 0.31), and hemoglobin A1c ( = 0.29) were associated with ECV fraction (all < 0.05); however, in multivariate linear regression analysis, SFRP2 was the only significant factor determined for ECV fraction ( = 0.33, = 0.02). In multivariate Cox regression analysis, age (each 10 years, hazard ratio (HR) 1.13, 95% confidence interval (CI) 1.04-1.22), ECV fraction (per doubling, HR 1.68, 95% CI 1.03-2.74), and NT-proBNP (per doubling, HR 2.46, 95% CI 1.05-5.76) were independent risk factors for primary outcomes.

Conclusions: Higher ECV fraction is associated with worsened prognosis in HF. SFRP2 is an independent biomarker for myocardial fibrosis. Further studies are needed to explore the potential therapeutic value of SFRP2 in myocardial fibrosis.
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http://dx.doi.org/10.1155/2020/2563508DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229555PMC
January 2021

Predictors of Poor Outcome in Patients with Minor Ischemic Stroke by Using Magnetic Resonance Imaging.

J Mol Neurosci 2019 Nov 20;69(3):478-484. Epub 2019 Jul 20.

Department of Neurology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), No.1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China.

Although the symptoms of minor ischemic stroke are mild, poor prognosis may occur if left untreated. Therefore, it is particularly important to identify the predictors that associated with poor outcome in patients presenting minor ischemic stroke. The aim of this study was to elucidate the predictors of progression by using magnetic resonance imaging (MRI). A total of 516 patients diagnosed with minor ischemic stroke were enrolled in this study. They were divided into two groups, the progressive group and non-progressive group, according to the modified Rankin Scale (mRS) with the cutoff value of 2 points on day 90 after the stroke onset. We compared the results of MRI scan between the two groups to investigate the potential independent determinants of progression using multivariate logistic regression analysis. Ninety of 516 patients (17.44%) underwent progression. There were 9 factors that were independently associated with poor outcome, including age (OR = 1.045, 95% CI 1.017-1.074), heart disease (OR = 2.021, 95% CI 1.063-3.841), baseline NIHSS score (OR = 1.662, 95% CI 1.177-2.347), limb motor disturbance (OR = 2.430, 95% CI 1.010-5.850), ataxia (OR = 2.929, 95% CI 1.188-7.221), early neurological deterioration (OR = 50.994, 95% CI 17.659-147.258), diameter of infarction (OR = 1.279, 95% CI 1.075-1.521), non-responsible vessel size (OR = 2.518, 95% CI 1.145-5.536), and large-artery atherosclerosis (OR = 2.010, 95% CI 1.009-4.003). This study indicated that age, heart disease, motor disturbance of limb, ataxia, early neurological deterioration, diameter of infarction, size of non-responsible vessels, and large-artery atherosclerosis can be used to assess the prognosis of patients with minor ischemic stroke.
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http://dx.doi.org/10.1007/s12031-019-01379-9DOI Listing
November 2019

Functional posterior communicating artery of patients with posterior circulation ischemia using phase contrast magnetic resonance angiography.

Exp Ther Med 2019 Jan 29;17(1):337-343. Epub 2018 Oct 29.

Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan, Guangdong 528308, P.R. China.

Posterior communicating artery (PCoA) in patients with posterior circulation ischemia (PCI) was quantitatively studied using phase contrast magnetic resonance angiography (PC-MRA). Fifty-one cases who received PC-MRA were included in the study, and divided into the research and control groups. The mean flow volume, mean flow velocity, minimum flow volume, maximum flow volume, minimum flow velocity and maximum flow velocity of the basal artery (BA), bilateral vertebral arteries, internal carotid arteries and functional posterior communicating artery (F-PCoA) were recorded, the peak heights of flow volume and flow velocity were calculated, and the typing of F-PCoA was analyzed, followed by statistical analysis. Fifty-two F-PCoAs were detected, and the median values of mean flow volume, mean flow velocity, cross-sectional area and lumen diameter were 20.31 ml/min, 4.01 cm/sec, 0.08 cm and 0.16 cm, respectively. The blood flow curve of F-PCoA showed the sawtooth-like changes, and there could be either unidirectional blood flow or bidirectional blood flow in one cardiac cycle. F-PCoA was divided into the following 3 types: F-PCoA was consistent with anatomical PCoA (A-PCoA), F-PCoA was inconsistent with A-PCoA, and mixed type. In the presence of F-PCoA, both the diameter and cross-sectional area of BA were small, and the maximum flow velocity and peak height of flow volume were reduced, but there was no necessary correlation with the occurrence of PCI. Both flow volume and flow velocity of BA in the research group were reduced, and the forward posterior shunt flow of F-PCoA was increased. Hemodynamic characteristics of F-PCoA can be analyzed via PC-MRA. The forward posterior shunt flow of F-PCoA can provide references for the clinical auxiliary diagnosis.
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http://dx.doi.org/10.3892/etm.2018.6897DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6307428PMC
January 2019
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