Lung Cancer Cytology: Can Any of the Cytological Methods Replace Histopathology?

Identification and validation of PCDHGA12 and PRRX1 methylation for detecting lung cancer in bronchial washing sample

Bronchoscopy is a frequently used initial diagnostic procedure for patients with suspected lung cancer (LC). Cytological examinations of bronchial washing (BW) samples obtained during bronchoscopy often yield inconclusive results regarding LC diagnosis. The present study aimed to identify molecular biomarkers as a non-invasive method for LC diagnosis. Aberrant DNA methylation is used as a useful biomarker for LC. Therefore, microarray-based methylation profiling analyses on 13 patient-matched tumor tissues at stages I-III vs. non-tumor tissues were performed, and a group of highly differentially methylated genes was identified. A subsequent analysis using bisulfite-pyrosequencing with additional tissues and cell lines revealed six methylated genes [ADAM metallopeptidase with thrombospondin type 1 motif 20, forkhead box C2 (mesenchyme forkhead 1), NK2 transcription factor related, locus 5 (Drosophila), oligodendrocyte transcription factor 3, protocadherin γ subfamily A 12 (PCDHGA12) and paired related homeobox 1 (PRRX1)] associated with LC. Next, a highly sensitive and accurate detection method, linear target enrichment-quantitative methylation-specific PCR in a single closed tube, was applied for clinical validation using BW samples from patients with LC (n=68) and individuals with benign diseases (n=33). PCDHGA12 and PRRX1 methylation were identified as the best-performing biomarkers to detect LC. The two-marker combination showed a sensitivity of 82.4% and a specificity of 87.9%, with an area under the curve of 0.891. Notably, the sensitivity for small cell LC was 100%. The two-marker combination had a positive predictive value of 93.3% and a negative predictive value of 70.7%. The sensitivity was higher than that of cytology, which only had a sensitivity of 50%. The methylation status of the two-marker combination showed no association with sex, age or stage, but was associated with tumor location and histology. In conclusion, the present study showed that the regulatory regions of PCDHGA12 and PRRX1 are highly methylated in LC and can be used to detect LC in BW specimens as a diagnostic adjunct to cytology in clinical practice.

Evaluating the Risk of Malignancies of the Diagnostic Categories Proposed by the World Health Organization System for Reporting Lung Cytopathology: A 2-Year Single Institutional Experience

INTRODUCTION

The WHO System of Reporting Lung Cytopathology proposed a 5-tiered system in 2023. We report the risk of malignancies (ROMs) of bronchial washing/lavage and percutaneous fine-needle aspiration (FNA) specimens. We also evaluated the change of ROMs when image correlation is required.

METHODS

Lung cytology cases in 2021 and 2022 with histologic follow-up were included. CT reports were reviewed to identify cases with a solid nodule/tumor but benign cytological findings. These were reassigned from the "benign" to "non-diagnostic" category, and the ROMs were re-estimated.

RESULTS

A total of 1,031 bronchial washing/lavage and 206 FNAs were identified. The ROMs of bronchial washing/lavage were "non-diagnostic" 56.5% (13/23), "benign" 41.9% (320/764), "atypical" 71.7% (71/99), "suspicious for malignancy" 94.7% (72/76), and "malignant" 100% (70/70). The ROMs of FNAs were "non-diagnostic" 66% (33/50), "benign" 58.2% (39/67), "atypical" 70% (28/40), "suspicious for malignancy" 96.2% (25/26), and "malignant" 100% (70/70). When image finding was considered, cases initially assigned as "benign" were re-classified to "non-diagnostic" with decreases in ROMs for the "benign" category.

CONCLUSIONS

Malignancy risks associated with the WHO System of Reporting Lung Cytopathology diagnostic groups were reported. Image correlation for the "benign" category led to a decrease in case number and ROM.

Atomic force microscopy-based assessment of multimechanical cellular properties for classification of graded bladder cancer cells and cancer early diagnosis using machine learning analysis

Cellular mechanical properties (CMPs) have been frequently reported as biomarkers for cell cancerization to assist objective cytology, compared to the current subjective method dependent on cytomorphology. However, single or dual CMPs cannot always successfully distinguish every kind of malignant cell from its benign counterpart. In this work, we extract 4 CMPs of four different graded bladder cancer (BC) cell lines by AFM (atomic force microscopy)-based nanoindentation to generate a CMP database, which is used to train a cancerization-grade classifier by machine learning. The classifier is tested on 4 categories of BC cells at different cancer grades. The classification shows split-independent robustness and an accuracy of 91.25% with an AUC-ROC (ROC stands for receiver operating characteristic, and ROC curve is a graphical plot which illustrates the performance of a binary classifier system as its discrimination threshold is varied) value of 97.98%. Finally, we also compare our proposed method with traditional invasive diagnosis and noninvasive cancer diagnosis relying on cytomorphology, in terms of accuracy, sensitivity and specificity. Unlike former studies focusing on the discrimination between normal and cancerous cells, our study fulfills the classification of 4 graded cell lines at different cancerization stages, and thus provides a potential method for early detection of cancerization. STATEMENT OF SIGNIFICANCE: We measured four cellular mechanical properties (CMPs) of 4 graded bladder cancer (BC) cell lines using AFM (atomic force microscopy). We found that single or dual CMPs cannot fulfill the task of BC cell classification. Instead, we employ MLA (Machine Learning Algorithm)-based analysis whose inputs are BC CMPs. Compared with traditional cytomorphology-based prognoses, the non-invasive method proposed in this study has higher accuracy but with many fewer cellular properties as inputs. The proposed non-invasive prognosis is characterized with high sensitivity and specificity, and thus provides a potential tumor-grading means to identify cancer cells with different metastatic potential. Moreover, our study proposes an objective grading method based on quantitative characteristics desirable for avoiding misdiagnosis induced by ambiguous subjectivity.

Importance of Cytopathologic Diagnosis in Early Cancer Diagnosis in Resource-Constrained Countries

PURPOSE

The rising cancer burden in low- and middle-income countries (LMICs) stresses already weak health care systems and poses unique challenges. In resource-constrained LMICs and in circumstances where most patients must pay out of pocket for diagnostic tests, these may not be available or affordable for many. Cytopathology provides a simple, inexpensive, standardized, and low-technology diagnostic procedure that is increasingly used as an effective tool to address the hurdles faced in cancer control programs in LMICs. This review explores the potential role of cytopathology in LMICs in reducing the cancer burden.

METHODS

This review studied the existing literature across the globe regarding the utilization of cytopathology as a diagnostic or screening tool for various types of malignancies as well as its advantages and disadvantages, depending on the local situation.

RESULTS

Apart from the usefulness of cytopathology, this review also sheds light on the barriers to using cytopathology in LMICs. Most recently, SARS-CoV-2 has produced several unique challenges for cytopathology. These are being met with innovative measures to combat the effects of the pandemic and ensure the safe delivery of essential cytopathology services.

CONCLUSION

The usefulness of cytopathologic techniques has been demonstrated via various studies, even during the recent pandemic. If cytology is to be used appropriately, the focus needs to be on integrating it into the national cancer screening and diagnostic programs as well as providing well-trained human resources.

Surgical Site Cytology to Diagnose Spinal Lesions

Patients with new-onset malignant spinal lesions often have an urgent need for local spine intervention and systemic therapy. For optimal management, it is crucial to diagnose the underlying disease as quickly and reliably as possible. The aim of our current study was to determine the feasibility, sensitivity, specificity, and diagnostic certainty of complementary cytological evaluation of spinal lesions suspected of malignancy. In 44 patients, we performed histopathological biopsies and in parallel cytologic preparations from the malignant site. Cytological smears were prepared and stained for May-Grunwald and Giemsa. Bone biopsies were histopathologically analyzed according to the existing standard-of-care practices. In 42 of 44 cases (95%), a cytological sample was successfully obtained. In 40 cases (95.2%, Cohen’s kappa: 0.77), the cytological diagnosis agreed with the histological diagnosis regarding the identification of a malignant lesion. This resulted in a sensitivity of 97% and a specificity of 80% as well as a diagnostic safety of 95%. Cytological analysis in the context of spinal surgery proved sufficient to establish a diagnosis of malignancy or its exclusion, expanding the existing diagnostic spectrum. Furthermore, implementation of this process as a routine clinical diagnostic might shorten the time to diagnosis and improve the treatment of this vulnerable patient group.

Acquired cystic disease-associated renal cell carcinoma: A clinicopathologic study of eight tumors with clinical follow-up

Acquired cystic disease-associated renal cell carcinoma (ACD-RCC) is the major RCC subtype in patients with end-stage renal disease, specifically those with ACD on dialysis. Three patients with a total of eight tumors have been selected. The aim of this study was to analyze clinicopathologic, immunohistochemical, and prognostic features of eight ACD-RCCs. Three patients with end-stage renal disease (ESRD) were in the age range of 34-45 years and being treated with hemodialysis. All eight tumors were resected by radical nephrectomy. Two patients had a single ACD-RCC, while one patient had bilateral and multifocal ACD-RCCs. Microscopically, combinations of architectural patterns were identified in all tumors. Intracytoplasmic and intraluminal vacuoles, eosinophilic granular cytoplasm, and prominent nucleoli were universal characteristics of these tumors. Atypical cysts were present in three out of four resected kidneys. Immunohistochemistry (IHC) staining revealed all tumors were strongly and diffusely positive for pan-cytokeratin and α-methylacyl-CoA racemase and variably positive for CK7, CD10, PAX8, EMA, vimentin, cytokeratin, high molecular weight cytokeratin (CK HMW). All cases were negative for Napsin A, CK20, CD117, and CD57. After an average follow-up of 27.5 months (range 3-54 months), all our patients are alive without neoplastic (metastatic or recurrent) disease. Our study supports the finding that ACD-RCC has specific morphologic features and a broad spectrum of architectural patterns. We have found that the immunoprofile of ACD-RCC is distinct from that in other RCCs; however, nonspecific and interpretation of microscopic features in the context of the clinical history can aid the diagnosis. We confirm also the favorable prognosis in ACD-RCC.

Mechanomics Biomarker for Cancer Cells Unidentifiable through Morphology and Elastic Modulus

Cellular mechanical properties are potential cancer biomarkers used for objective cytology to replace the current subjective method relying on cytomorphology. However, heterogeneity among intra/intercellular mechanics and the interplay between cytoskeletal prestress and elastic modulus obscured the difference detectable between malignant and benign cells. In this work, we collected high density nanoscale prestress and elastic modulus data from a single cell by AFM indentation to generate a cellular mechanome. Such high dimensional mechanome data was used to train a malignancy classifier through machine learning. The classifier was tested on 340 single cells of various origins, malignancy, and degrees of similarity in morphology and elastic modulus. The classifier showed instrument-independent robustness and classification accuracy of 89% with an AUC-ROC value of 93%. A signal-to-noise ratio 8 times that of the human-cytologist-based morphological method was also demonstrated, in differentiating precancerous hyperplasia cells from normal cells derived from the same lung cancer patient.