Publications by authors named "Songqi Cai"

4 Publications

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Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.

Eur Radiol 2021 Apr 16. Epub 2021 Apr 16.

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.

Objectives: To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC).

Methods: In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set.

Results: In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively).

Conclusions: The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status.

Key Points: • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.
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http://dx.doi.org/10.1007/s00330-021-07902-0DOI Listing
April 2021

MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.

Eur Radiol 2021 Jan 2;31(1):403-410. Epub 2020 Aug 2.

Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Jinshan District, Shanghai, 201508, China.

Objectives: Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC.

Methods: In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation.

Results: The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection.

Conclusions: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis.

Key Points: • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.
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http://dx.doi.org/10.1007/s00330-020-07091-2DOI Listing
January 2021

MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study.

J Magn Reson Imaging 2020 09 11;52(3):897-904. Epub 2020 Feb 11.

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.

Background: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results.

Purpose: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation.

Study Type: Retrospective study of eight clinical centers.

Population: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159).

Field Strength/sequence: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T -weighted imaging (T WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T WI.

Assessment: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images.

Statistical Tests: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).

Results: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts.

Data Conclusion: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women.

Level Of Evidence: Level 4.

Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.
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http://dx.doi.org/10.1002/jmri.27084DOI Listing
September 2020

Incidence of insulin resistance and diabetes in patients with portosystemic shunts without liver dysfunction.

J Int Med Res 2016 Oct 29;44(5):1040-1048. Epub 2016 Sep 29.

3 Department of Gastroenterology, Jinshan Hospital, Fudan University, Shanghai, China.

Objective To investigate the incidence of insulin resistance (IR) and diabetes in patients with chronic hepatic schistosomiasis japonica (HSJ) and portosystemic shunts (PSS). Methods Pre- and post-contrasted computed tomography images obtained from patients with HSJ and control subjects were reviewed by two radiologists who identified and graded any shunting vessels. Anthropometric measurements, hepatic enzymes, lipid profile, blood levels of albumin, glucose, insulin and homeostasis model assessment (HOMA-2) index of all participants were also assessed. Results Fifty-two patients with HSJ and 30 control subjects were involved in the study. The coronary, short gastric and perisplenic veins were the most common shunting vessels. There were no significant differences between patients and controls in terms of body mass index or liver function. The degree of shunting vessels, blood glucose, oral glucose tolerance test, insulin, HOMA-2 index, glycosylated haemoglobin, cholesterol, high- and low-density lipoprotein, and C-reactive protein were significantly higher in the patients with IR. A positive correlation was found between the degree of the shunting vessels and the HOMA-2 index. Conclusions Patients with chronic HSJ and PSS without liver dysfunction had a high incidence of IR and diabetes. The study showed that PSS and IR are related and therefore patients with PSS should be screened for IR and vice versa.
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http://dx.doi.org/10.1177/0300060516659392DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5536557PMC
October 2016