[A consensus on prenatal diagnosis and genetic counseling for chromosomal mosaicism].

With the extensive application of highly sensitive genetic techniques in the field of prenatal diagnosis, prenatal chromosomal mosaicisms including true fetal mosaicisms and confined placental mosaicisms are frequently identified in clinical settings, and the diagnostic criteria and principle of genetic counseling and clinical management for such cases may vary significantly among healthcare centers across the country. This not only has brought challenges to laboratory technician, genetic counselor and fetal medicine doctor, but can also cause confusion and anxiety of the pregnant woman and their family members. In this regard, we have formulated a consensus over the prenatal diagnosis and genetic counseling for chromosomal mosaicisms with the aim to promote more accurate and rational evaluation for fetal chromosomal mosaicisms in prenatal clinics.

[Clinical characteristics and prenatal diagnosis of fetuses with sex chromosomal aneuploidies detected by non-invasive prenatal testing during early and midterm pregnancies].

OBJECTIVE: To analyze the indication, karyotyping result, ultrasound finding, pregnancy decision and follow-up of fetuses with sex chromosome aneuploidies (SCA) detected by non-invasive prenatal testing (NIPT) during early and midterm pregnancies. METHODS: The results of 225 singleton pregnancies with fetal SCA detected by NIPT were reviewed and analyzed. RESULTS: The 225 cases included 45,X (n=37), 47,XXY (n=74), 47,XXX (n=50), 47,XYY (n=56) and mosaicisms (n=8), among which 121 (53.8%) have opted to terminate the pregnancy, including 45,X (n=31), 47,XXY (n=61), 47,XXX (n=14), 47,XYY (n=12) and 3 mosaicisms. The remainder 104 (46.2%) have elected to continue with the pregnancy, among which three have opted to terminate due to abnormalities detected by ultrasonography, and two had spontaneous abortions. CONCLUSION: NIPT as a first-tier screening method can effectively detect fetal trisomies 21, 13 and 18 as well as SCA. The types of fetal SCA and presence of ultrasound abnormalities are critical factors for the termination of pregnancy.

[Combined chromosomal microarray analysis and fluorescence in situ hybridization for prenatal diagnosis of two cases with Pallister-Killian syndrome].

OBJECTIVE: To carry out prenatal diagnosis for two cases of Pallister-Killian syndrome (PKS) using combined chromosomal microarray analysis (CMA) and fluorescence in situ hybridization (FISH). METHODS: Umbilical cord blood was sampled from the two fetuses and subjected to G-banding chromosomal karyotyping, CMA and FISH assay. RESULTS: Chromosomal karyotyping showed that the two fetuses were mos 47,XX,+i(12)(p10)[3]/46,XX[197] and mos 47,XY,+i(12)(p10)[5]/46,XY[95], respectively. CMA showed that both had carried duplication of 12p. The results of interphase FISH confirmed mosaicism of 12p tetrasomy. Combined with ultrasonographic findings, both fetuses were diagnosed as PKS. CONCLUSION: Prenatal ultrasound examination, karyotype analysis of umbilical cord blood, G-banded chromosomal analysis, CMA and FISH may be used in conjunct for the prenatal diagnosis of PKS.

[Application of chromosomal karyotyping analysis and array CGH for fetal abnormalities detected by ultrasonography].

OBJECTIVE: To assess the value of chromosomal karyotyping and array-based comparative genomic hybridization for the diagnosis of fetus with abnormalities detected by ultrasonography. METHODS: Umbilical cord blood samples were derived from 1 603 pregnant women. The samples were cultured for routine G-banding karyotype analysis. Among these, 792 samples have further subjected to array CGH analysis. RESULTS: Among the 1 603 fetuses, 117 (7.30%) were found with chromosomal abnormalities. These included 72 numerical aberrations and 45 structural abnormalities, which respectively accounted for 4.49% and 2.81% of all cases. For those <35 years and ≥ 35 years, a significant difference has been found in terms of fetal chromosomal abnormalities (chi-square is 30.687, P< 0.01). And there was also a significant difference between those with isolated, two or multiple ultrasonographic markers (chi-square is 85.50, P< 0.01). Among 736 fetuses with a normal karyotype, array CGH has detected 17 (2.31%) with a microdeletion or microduplication. CONCLUSION: Karyotype analysis and array CGH should be offered to all fetuses with ultrasonography detected anomalies regardless the number of markers.

A Clinical Dataset and Various Baselines for Chromosome Instance Segmentation.

BACKGROUND: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. METHOD: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (CILA) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. RESULTS AND CONCLUSIONS: Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with 77% mAP, 97.5% AP50, and 95.5% AP75 segmentation precision, and 95.38% accuracy, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.

ChromosomeNet: A massive dataset enabling benchmarking and building basedlines of clinical chromosome classification.

Chromosome karyotyping analysis is a vital cytogenetics technique for diagnosing genetic and congenital malformations, analyzing gestational and implantation failures, etc. Since the chromosome classification as an essential stage in chromosome karyotype analysis is a highly time-consuming, tedious, and error-prone task, which requires a large amount of manual work of experienced cytogenetics experts. Many deep learning-based methods have been proposed to address the chromosome classification issues. However, two challenges still remain in current chromosome classification methods. First, most existing methods were developed by different private datasets, making these methods difficult to compare with each other on the same base. Second, due to the absence of reproducing details of most existing methods, these methods are difficult to be applied in clinical chromosome classification applications widely. To address the above challenges in the chromosome classification issue, this work builds and publishes a massive clinical dataset. This dataset enables the benchmarking and building chromosome classification baselines suitable for different scenarios. The massive clinical dataset consists of 126,453 privacy preserving G-band chromosome instances from 2763 karyotypes of 408 individuals. To our best knowledge, it is the first work to collect, annotate, and release a publicly available clinical chromosome classification dataset whose data size scale is also over 120,000. Meanwhile, the experimental results show that the proposed dataset can boost performance of existing chromosome classification models at a varied range of degrees, with the highest accuracy improvement by 5.39 % points. Moreover, the best baseline with 99.33 % accuracy reports state-of-the-art classification performance. The clinical dataset and state-of-the-art baselines can be found at https://github.com/CloudDataLab/BenchmarkForChromosomeClassification.

CIR-Net: Automatic Classification of Human Chromosome Based on Inception-ResNet Architecture.

BACKGROUND: In medicine, karyotyping chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosome karyotyping is usually done by skilled cytologists manually, which requires experience, domain expertise, and considerable manual efforts. Therefore, automating the karyotyping process is a significant and meaningful task. METHOD: This paper focuses on chromosome classification because it is critical for chromosome karyotyping. In recent years, deep learning-based methods are the most promising methods for solving the tasks of chromosome classification. Although the deep learning-based Inception architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge, it has not been used in chromosome classification tasks so far. Therefore, we develop an automatic chromosome classification approach named CIR-Net based on Inception-ResNet which is an optimized version of Inception. However, the classification performance of origin Inception-ResNet on the insufficient chromosome dataset still has a lot of capacity for improvement. Further, we propose a simple but effective augmentation method called CDA for improving the performance of CIR-Net. RESULTS: The experimental results show that our proposed method achieves 95.98 percent classification accuracy on the clinical G-band chromosome dataset whose training dataset is insufficient. Moreover, the proposed augmentation method CDA improves more than 8.5 percent (from 87.46 to 95.98 percent) classification accuracy comparing to other methods. In this paper, the experimental results demonstrate that our proposed method is recent the most effective solution for solving clinical chromosome classification problems in chromosome auto-karyotyping on the condition of the insufficient training dataset. Code and Dataset are available at https://github.com/CloudDataLab/CIR-Net.

A novel chromosome cluster types identification method using ResNeXt WSL model.

Chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, due to the complicated morphological characteristics of various types of chromosome clusters, chromosome instance segmentation is the most challenging stage of chromosome karyotyping analysis, leading chromosome karyotyping analysis to highly dependent on skilled clinical analysts. Since most of the chromosome instance segmentation efforts are currently devoted to segmenting chromosome instances from different types of chromosome clusters, type identification of chromosome clusters is a vital anterior task for chromosome instance segmentation. Firstly, this paper proposes an automatic approach for chromosome cluster identification using recent transfer learning techniques. The proposed framework is based on ResNeXt weakly-supervised learning (WSL) pre-trained backbone and a task-specific network header. Secondly, this paper proposes a fast training methodology that tunes our framework from coarse-to-fine gradually. Extensive evaluations on a clinical dataset consisting of 6592 clinical chromosome samples show that the proposed framework achieves 94.09%accuracy, 92.79%sensitivity, and 98.03%specificity. Such performance is superior to the best baseline model that we obtain 92.17%accuracy, 89.1%sensitivity, and 97.42%specificity. To foster research and application in the chromosome cluster type identification, we make our clinical dataset and code available via GitHub.

Prenatal diagnosis of Pallister-Killian syndrome using cord blood samples.

BACKGROUND: Pallister-Killian syndrome (PKS) (OMIM:#601803) is a rare sporadic genetic disorder characterized by multi-malformations which is caused by the presence of the extra isochromosome 12p. PKS is featured by the tissue-limited mosaicism of the isochromosome 12p [i(12p)]. There were a wide spectrum of prenatal ultrasound findings of PKS, which made it difficult to be found in first or second trimester. Polyhydramnios, diaphragmatic hernia, and rhizomelic limb shortening were the most common prenatal ultrasound abnormalities in PKS. This study retrospectively analyzed the ultrasound findings and molecular cytogenetic results of four PKS fetuses diagnosed by using cord blood samples. RESULTS: The ultrasound anomalies of four PKS fetuses are described as follows: fetal macrosomia, cerebral ventriculomegaly, increased NT thickness, rhizomelic limbs shortening, polyhydramnios. Biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL) measurements were above the mean in three fetuses,while one fetus showed rhizomelic limbs shortening. Combined with this study and previous literature, polyhydramnios was the most frequent anomaly observed in prenatal ultrasound examination of PKS, which accounted for 48% (94/194). Fetal macrosomia was present in 15% (29/194), cerebral ventriculomegaly in 13% (25/194), thickened nuchal fold in 9% (18/194), rhizomelic limbs shortening in 26% (51/194). I(12p) was found in the karyotype analysis of cultured cord blood lymphocytes and the mosaic ratios ranged from 2 to 5%. Single nucleotide polymorphisms array (SNP-array) results suggested that the whole short arm of chromosome 12 was duplicated with 2~3 copies. Fluorescence in situ hybridization (FISH) was performed to confirm the results of karyotype and SNP-array. CONCLUSIONS: In case non-specific indicators such as fetal macrosomia, polyhydramnios and rhizomelic limbs shortening are observed meanwhile in prenatal ultrasound, targeted detection of PKS should be considered. In the prenatal diagnosis of PKS, the combination of SNP-array and FISH with conventional karyotype are the key to seek i(12p) and for precise diagnosis.

Discrepancy of QF-PCR, CMA and karyotyping on a de novo case of mosaic isodicentric Y chromosomes.

BACKGROUND: Isodicentric chromosomes are the most frequent structural aberrations of human Y chromosome, and usually present in mosaicism with a 45, X cell line. Several cytogenetic techniques have been used for diagnosing of uncommon abnormal sex chromosome abnormalities in prenatal cases. CASE PRESENTATION: A 26-year-old healthy woman was referred to our centre at 24 weeks of gestation age. Ultrasound examination indicated she was pregnant with imbalanced development of twins. Amniocentesis was referred to the patient for further genetic analyses. Quantitative Fluorescent Polymerase Chain Reaction (QF-PCR) indicated the existence of an extra Y chromosome or a structurally abnormal Y chromosome in primary amniotic cells. Chromosome microarray (CMA) analysis based on Comparative Genomic Hybridization (aCGH) platform was performed and identified a 10.1 Mb deletion on Y chromosome in 8-days cultured amniotic cells. Combined with the data of QF-PCR and aCGH, karyotyping and fluorescence in situ hybridization (FISH) revealed a mosaic cell line of 45,X[27]/46,X, idic(Y)(q11.22) [14] in fetus.The karyotyping analysis of cord blood sample was consistent with amniotic cells. The parental karyotypes were normal, which indicated this mosaic case of isodicentric Y (idicY) chromosomes of the fetus was a de novo case. CONCLUSION: Several approaches have been used for the detection of numerical and structural chromosomal alterations of on prenatal cases. Our report supported the essential role of incorporating multiple genetic techniques in prenatal diagnosing and genetic counseling of potential complex sex chromosomal rearrangements.