Zhen Li

Achieving flexible fairness metrics in federated medical imaging

The rapid adoption of Artificial Intelligence (AI) in medical imaging raises fairness and privacy concerns across demographic groups, especially in diagnosis and treatment decisions. While federated learning (FL) offers decentralized privacy preservation, current frameworks often prioritize collaboration fairness over group fairness, risking healthcare disparities. Here we present FlexFair, an innovative FL framework designed to address both fairness and privacy challenges. FlexFair incorporates a flexible regularization term to facilitate the integration of multiple fairness criteria, including equal accuracy, demographic parity, and equal opportunity. Evaluated across four clinical applications (polyp segmentation, fundus vascular segmentation, cervical cancer segmentation, and skin disease diagnosis), FlexFair outperforms state-of-the-art methods in both fairness and accuracy. Moreover, we curate a multi-center dataset for cervical cancer segmentation that includes 678 patients from four hospitals. This diverse dataset allows for a more comprehensive analysis of model performance across different population groups, ensuring the findings are applicable to a broader range of patients.

The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis

The primary aim of this study was to conduct a methodical examination and assessment of the prognostic efficacy exhibited by magnetic resonance imaging (MRI)-derived radiomic models concerning the preoperative prediction of lymph-vascular space infiltration (LVSI) in cervical cancer cases. A comprehensive and thorough exploration of pertinent academic literature was undertaken by two investigators, employing the resources of the Embase, PubMed, Web of Science, and Cochrane Library databases. The scope of this research was bounded by a publication cutoff date of May 15, 2023. The inclusion criteria encompassed studies that utilized radiomic models based on MRI to prognosticate the accuracy of preoperative LVSI estimation in instances of cervical cancer. The Diagnostic Accuracy Studies-2 framework and the Radiomic Quality Score metric were employed. This investigation included nine distinct research studies, enrolling a total of 1,406 patients. The diagnostic performance metrics of MRI-based radiomic models in the prediction of preoperative LVSI among cervical cancer patients were determined as follows: sensitivity of 83% (95% confidence interval [CI]=77%-87%), specificity of 74% (95% CI=69%-79%), and a corresponding AUC of summary receiver operating characteristic measuring 0.86 (95% CI=0.82-0.88). The results of the synthesized meta-analysis did not reveal substantial heterogeneity.This meta-analysis suggests the robust diagnostic proficiency of the MRI-based radiomic model in the prognostication of preoperative LVSI within the cohort of cervical cancer patients. In the future, radiomics holds the potential to emerge as a widely applicable noninvasive modality for the early detection of LVSI in the context of cervical cancer.

Comparison of reduced field-of-view diffusion-weighted imaging (DWI) and conventional DWI techniques in the assessment of Cervical carcinoma at 3.0T: Image quality and FIGO staging

To evaluate imaging quality (IQ) and International Federation of Gynecology and Obstetrics (FIGO) staging of reduced field-of-view (r-FOV) diffusion-weighted imaging (DWI) in cervical carcinoma (CC). Sixty patients with pathologically proven CC who underwent both pre-treatment r-FOV DWI and full field-of-view (f-FOV) DWI on a 3.0T MRI scanner were retrospectively reviewed. The subjective qualitative image scores were compared using the Wilcoxon signed-rank test. Objective quality values and apparent diffusion coefficient (ADC) were estimated by paired t-test or Wilcoxon signed-rank test for the two DWI sequences according to Normality test. Spearman rank correlation analysis was used to evaluate the relationship between pathological results and mean ADC value. The subjective IQ scores for r-FOV DWI were significantly higher than those for f-FOV DWI (P < 0.001). Similarly, the contrast-to-noise (CNR) value of r-FOV DWI was superior to that of f-FOV DWI (10.30 ± 3.676, 8.91 ± 3.008, P = 0.021). However, the signal-to-noise ratio (SNR) value of r-FOV DWI was considerably lower than that of f-FOV DWI (27.80 ± 6.056, 33.67 ± 7.833, P＜0.001). No significant difference was found between mean ADC values of f-FOV DWI and r-FOV DWI. There was a significant tendency for a negative correlation between the ADC values and FIGO stages of CC for both two sequences (r=-0. 436, P＜0.01; r=-0.470, P＜0.01, respectively). The rFOV DWI sequence provided significantly better IQ and lesion conspicuity than the fFOV DWI sequence. In addition, rFOV sequences can be used in evaluation of FIGO staging of cervical cancer.