Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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The American journal of pathology
Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology
Cytology cell block specimens are essential for diagnosing patients with advanced-stage malignancy and often represents the only available strategy for therapeutic biomarker evaluation. The use of cell blocks preserves tumor cells, captures high-grade or metastatic populations, and retains meaningful microenvironmental context, making them well suited for IHC analysis. With the rapid expansion of computational pathology, deep learning-assisted biomarker interpretation in cell blocks is emerging as a promising frontier for improving reproducibility, reducing interobserver variability, and enabling quantitative assessment of spatial tumor-immune interactions. Because many treatment-defining biomarkers are routinely assessed on cytology cell blocks, this mini-review highlights artificial intelligence-based applications for PD-L1, HER2, ER/PR, Ki-67, ALK/ROS1, BRAF V600E, and p16 markers that directly inform decisions about immunotherapy, targeted therapy, and hormone therapy. Also reviewed are emerging predictive models that convey biomarker status directly from morphology, extending the utility of artificial intelligence beyond conventional IHC interpretation. Finally, spatial pathobiology-related opportunities afforded by cell block preparations are discussed, and future directions for integrating artificial intelligence-enabled analysis into cytology workflows are outlined. Together, these advances position cytology cell blocks as an important platform for computational biomarker interpretation and morphology-driven precision oncology.
2 July 2026
Read appraisal →Cytopathology : official journal of the British Society for Clinical Cytology
Comparative Evaluation of Transfer Learning Models for Detecting Malignant Cells in Urinary Cytology
AIMS AND OBJECTIVES: In the present paper, we compared the efficiency of six transfer learning models to detect malignant cells in urine cytology. We also applied an ensemble learning with weighted soft voting to assess its importance in the diagnostic accuracy in urine cytology. MATERIALS AND METHODS: The voided urine samples of total 104 cases of urothelial cell carcinoma (UCC) and 86 cases with no malignancy (benign) were selected. All the 104 cases of UCC were histopathology proven high grade urothelial cell carcinoma (UCC). Urine with negative cytology reports were followed up clinically. There were 446 images of benign samples and 1369 images from malignant samples on 100 x objective. We applied six transfer learning models (DenseNet121, inception_v3, ResNet50, MobileNetV2, VGG16 and Xception) to detect malignant cells in urine. To compare the performance of different models, dynamic training optimization was done and each model was auto stopped after their maximum performance. In addition, an ensemble learning with soft voting was used including the top three models to enhance the diagnostic accuracy. RESULT: Xception transfer model showed the highest sensitivity (88.57%), accuracy (86.55%), precision (80.52%) and FI score (84.35%). It was the best performing model. The other two top performing models were InceptionV3 and ResNet50. The area under curve of receiver operating characteristic (AUCROC) was ≤ 90 in all the transfer learning models. The accuracy, sensitivity, specificity, precision, F1-Score and AUCROC of the ensemble transfer learning model were as follows: 92.10%, 95.41%, 85.51%, 91.23%, 93.24% and 0.977 respectively. CONCLUSIONS: First time, we evaluated a large number of transfer learning models in urine cytology to detect malignant cells. All the models showed high sensitivity, specificity and accuracy. In addition, the ensemble learning technique with soft voting showed remarkable superior performance than individual top three transfer learning models. The techniques transfer learning and ensemble models have high potential to use in routine screening of urine.
6 May 2026
Read appraisal →Cytopathology : official journal of the British Society for Clinical Cytology
Application of Artificial Intelligence-Based Transfer Learning Models to the Bethesda System for Thyroid Cytopathology
BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) provides a standardised framework for thyroid fine needle aspiration cytology (FNAC). Deep learning approaches, particularly transfer learning, have shown potential for cytology but are rarely applied to TBSRTC categorisation. AIMS AND OBJECTIVES: To evaluate the performance of an ensemble soft voting transfer learning model in categorising thyroid cytology smears according to TBSRTC. MATERIALS AND METHODS: A retrospective study of 94 thyroid FNAC cases with 949 representative images was conducted. Six transfer learning models (Xception, ResNet50V2, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB3) were combined using ensemble soft voting with a weighted average. Model performance was assessed using sensitivity, specificity, precision (PPV), negative predictive value (NPV), F1 score and area under the receiver operating characteristic curve (AUCROC). RESULTS: DenseNet121 achieved the highest overall sensitivity (0.91) and specificity (0.98) among all single models. The weighted ensemble model achieved an F1 score of 0.867, marginally below DenseNet121 but with superior precision (0.882) and NPV (0.968). AUCROC was highest in DenseNet 121 (0.99) followed by the weighted ensemble (0.98). CONCLUSION: This is the first study to apply ensemble transfer learning to TBSRTC categorisation. The approach demonstrated strong predictive accuracy, particularly for benign and malignant categories. Larger datasets, ideally with whole-slide imaging, are needed to further validate these findings.
4 May 2026
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