Efficiency of Non-Invasive Prenatal Testing in Detecting Fetal Copy Number Variation: A Retrospective Cohort Study

Li Yang,1 Jing Yang,2 Guosen Bu,3 Rui Han,1 Jiamila Rezhake,1 Xiaolin La1 1Center of Reproductive Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China; 2Department of Gynaecology, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People’s Republic of China; 3Department of Neurology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of ChinaCorrespondence: Xiaolin La, Center of Reproductive Medicine, The First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushannan Road, Urumqi, 830054, People’s Republic of China, Tel +86-991-4362889, Email [email protected]: Screening of pathological copy number variations (CNVs) is important for early-diagnosis of hereditary disease. This study was designed to investigate the efficiency of non-invasive prenatal testing (NIPT) in detecting fetal CNVs.Methods: This retrospective analysis included fetuses with CNVs between January 2018 and December 2020. Karyotype analysis and CNV sequencing (CNV-seq) were performed. We then analyzed the positive predictive values of the subchromosomal microdeletions and microduplications.Results: Fifty-eight subjects with aberrant CNVs were screened after NIPT, among which 44 finally underwent amniocentesis. CNV-seq confirmed the presence of CNVs in 24 cases. This indicated that false positivity rate of NIPT was 45.5%. Among 24 cases with CNVs after CNV-seq, only 4 showed consistent findings with karyotype analysis, which showed that karyotyping analysis yielded a missed diagnosis rate of 83.3% for the genome CNV. Positive predictive value (PPV) was 50.0% for CNVs with a length of < 5 Mb after NIPT screening. PPV for CNVs with a length of 5 Mb-10 Mb was 33.3%, while that for CNVs with a length of ≥ 10Mb was 60%. For CNVs duplication after NIPT, the PPV was 65.2%, while that for deletion was 36.4%.Conclusion: For CNVs detected after NIPT, it should be combined with ultrasonographic findings, karyotype analysis, CNV-seq or CMA to determine the pregnancy outcome. Expanding NIPT may increase the risk of unnecessary invasive surgery and unintended selective termination of pregnancy.Keywords: non-invasive prenatal screening, genome copy number variation, next-generation sequencing, prenatal diagnosi

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics:Systematic Review With Meta-analysis

BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE: We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS: PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS: In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS: The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION: PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372