Positive urinary cytology in patients with lung cancer in the absence of obvious urine tract metastases

Machine learning assisted microfluidics dual fluorescence flow cytometry for detecting bladder tumor cells based on morphological characteristic parameters

BACKGROUND

Bladder cancer (BC) is the most common malignant tumor and has become a major public health problem, leading the causes of death worldwide. The detection of BC cells is of great significance for clinical diagnosis and disease treatment. Urinary cytology based liquid biopsy remains high specificity for early diagnosis of BC, however, it still requires microscopy examination which heavily relies on manual operations. It is imperative to investigate the potential of automated and indiscriminate cell differentiation technology to enhance the sensitivity and efficiency of urine cytology.

RESULTS

Here, we developed a machine learning algorithm empowered dual-fluorescence flow cytometry platform (μ-FCM) for urinary cytology analysis. A phenotype characteristic parameter (CP) which correlated with the size of the cell and nucleus was defined to achieve the differentiation of the BC cells and uroepithelial cells with high throughput and high accuracy. Based on CP analysis, SV-HUC-1 cells were almost differentiated from EJ cells and effectively reduced the overlap with 5637 cells. To further differentiate SV-HUC-1 cells and 5637 cells, support vector machine (SVM) machine learning algorithm was optimized to assist data analysis with the highest accuracies of 84.7 % for cell differentiation including the specificity of 91.0 % and the sensitivity of 75.0 %. Furthermore, the false positive rate (FPR) compensation enabled the detection rates of rare BC cells predicted by the well-trained SVM model were close to the true proportions with the recognition error in 0.4 % for the tumor cells.

SIGNIFICANCE

As a proof of concept, the developed μ-FCM system successfully demonstrates the capacity to identify the distribution of exfoliated cells in real urine samples. This system underscores the significance of integrating AI with microfluidics to perform high-throughput phenotyping of exfoliated cells, offering a pathway toward scalable, efficient, and automatic microfluidic systems in the fields of both biosensing and in vitro diagnosis of BC.

An Approach to Nonurothelial Malignancies of the Urinary Bladder in Urine Cytology

Urine cytology is an economical and convenient method of triaging patients who present with urinary symptoms as well as surveying those who have previously been diagnosed with urothelial carcinoma for recurrent or persistent disease. While the vast majority of malignancies diagnosed in urine cytology are urothelial carcinomas, it is important to recognize nonurothelial elements to inform patient prognosis and raise the possibility of involvement by a urothelial carcinoma variant, nonurothelial malignancy of the bladder, or a nonbladder primary, which may alter patient management pathways. As such, becoming familiar with morphologic features of nonurothelial malignancies in urine cytology as well as their related clinical risk factors, radiologic and cystoscopic features, differential diagnostic considerations, and the utility and pitfalls of ancillary tests can facilitate optimal patient care.

Hematuria in breast cancer: don't forget bladder metastases!

The bladder is a rare site for breast cancer metastases, and only occasional reports are present in the literature. Most cases coexist with synchronous metastases elsewhere, but isolated cases of a single metastatic localization in the urinary bladder have been reported. The most common symptoms of a metastatic localization of breast cancer to the urinary bladder are hematuria and voiding dysfunction. Herein we present three cases of urinary bladder metastasis from breast carcinoma, all presenting with gross hematuria as the only symptom. After a review of the relevant literature, we discuss the clinical and histological characteristics unique to our cases, highlighting potential clinical and pathological diagnostic pitfalls and differential diagnoses.