Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models
<b>Background:</b> Malignant pleural effusions (MPEs) are common in advanced lung cancer patients. Cytological examination of pleural fluid is essential for identifying cell types but presents diagnostic challenges, particularly when reactive mesothelial cells mimic neoplastic cells. AI-...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2075-4418/15/10/1240 |
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| author | Enrico Giarnieri Elisabetta Carico Stefania Scarpino Alberto Ricci Pierdonato Bruno Simone Scardapane Daniele Giansanti |
| author_facet | Enrico Giarnieri Elisabetta Carico Stefania Scarpino Alberto Ricci Pierdonato Bruno Simone Scardapane Daniele Giansanti |
| author_sort | Enrico Giarnieri |
| collection | DOAJ |
| description | <b>Background:</b> Malignant pleural effusions (MPEs) are common in advanced lung cancer patients. Cytological examination of pleural fluid is essential for identifying cell types but presents diagnostic challenges, particularly when reactive mesothelial cells mimic neoplastic cells. AI-powered diagnostic systems have emerged as valuable tools in digital cytopathology. This study explores the applicability of machine-learning (ML) models and highlights the importance of accessible tools for clinicians, enabling them to develop AI solutions and make advanced diagnostic tools available even in resource-limited settings. The focus is on differentiating normal/reactive cells from neoplastic cells in pleural effusions linked to lung adenocarcinoma. <b>Methods:</b> A dataset from the Cytopathology Unit at the Sant’Andrea University Hospital comprising 969 raw images, annotated with 3130 single mesothelial cells and 3260 adenocarcinoma cells, was categorized into two classes based on morphological features. Object-detection models were developed using YOLOv8 and the latest YOLOv11 instance segmentation models. <b>Results:</b> The models achieved an Intersection over Union (IoU) score of 0.72, demonstrating robust performance in class prediction for both categories, with YOLOv11 showing performance improvements over YOLOv8 in different metrics. <b>Conclusions:</b> The application of machine learning in cytopathology offers clinicians valuable support in differential diagnosis while also expanding their ability to engage with AI tools and methodologies. The diagnosis of MPEs is marked by substantial morphological and technical variability, underscoring the need for high-quality datasets and advanced deep-learning models. These technologies have the potential to enhance data interpretation and support more effective clinical treatment strategies in the era of precision medicine. |
| format | Article |
| id | doaj-art-5485e4043b9e4e4b8c4cf93b0f3c9111 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-5485e4043b9e4e4b8c4cf93b0f3c91112025-08-20T03:14:31ZengMDPI AGDiagnostics2075-44182025-05-011510124010.3390/diagnostics15101240Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly ModelsEnrico Giarnieri0Elisabetta Carico1Stefania Scarpino2Alberto Ricci3Pierdonato Bruno4Simone Scardapane5Daniele Giansanti6Cytopathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, ItalyCytopathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, ItalyMorphologic and Molecular Pathology Unit, Department of Clinical and Molecular Medicine, Sant’ Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, ItalyRespiratory Disease Unit, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, ItalyRespiratory Disease Unit, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, ItalyDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Via Eudossiana 18, 00196 Rome, ItalyCentre Tisp, ISS, 00161 Rome, Italy<b>Background:</b> Malignant pleural effusions (MPEs) are common in advanced lung cancer patients. Cytological examination of pleural fluid is essential for identifying cell types but presents diagnostic challenges, particularly when reactive mesothelial cells mimic neoplastic cells. AI-powered diagnostic systems have emerged as valuable tools in digital cytopathology. This study explores the applicability of machine-learning (ML) models and highlights the importance of accessible tools for clinicians, enabling them to develop AI solutions and make advanced diagnostic tools available even in resource-limited settings. The focus is on differentiating normal/reactive cells from neoplastic cells in pleural effusions linked to lung adenocarcinoma. <b>Methods:</b> A dataset from the Cytopathology Unit at the Sant’Andrea University Hospital comprising 969 raw images, annotated with 3130 single mesothelial cells and 3260 adenocarcinoma cells, was categorized into two classes based on morphological features. Object-detection models were developed using YOLOv8 and the latest YOLOv11 instance segmentation models. <b>Results:</b> The models achieved an Intersection over Union (IoU) score of 0.72, demonstrating robust performance in class prediction for both categories, with YOLOv11 showing performance improvements over YOLOv8 in different metrics. <b>Conclusions:</b> The application of machine learning in cytopathology offers clinicians valuable support in differential diagnosis while also expanding their ability to engage with AI tools and methodologies. The diagnosis of MPEs is marked by substantial morphological and technical variability, underscoring the need for high-quality datasets and advanced deep-learning models. These technologies have the potential to enhance data interpretation and support more effective clinical treatment strategies in the era of precision medicine.https://www.mdpi.com/2075-4418/15/10/1240cytopathologycytologypleural effusionlung adenocarcinomamachine learningYOLOv8 |
| spellingShingle | Enrico Giarnieri Elisabetta Carico Stefania Scarpino Alberto Ricci Pierdonato Bruno Simone Scardapane Daniele Giansanti Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models Diagnostics cytopathology cytology pleural effusion lung adenocarcinoma machine learning YOLOv8 |
| title | Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models |
| title_full | Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models |
| title_fullStr | Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models |
| title_full_unstemmed | Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models |
| title_short | Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models |
| title_sort | bringing ai to clinicians simplifying pleural effusion cytology diagnosis with user friendly models |
| topic | cytopathology cytology pleural effusion lung adenocarcinoma machine learning YOLOv8 |
| url | https://www.mdpi.com/2075-4418/15/10/1240 |
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