Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma
Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL)...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/14/2451 |
| _version_ | 1797589662200496128 |
|---|---|
| author | Fabrizio Gozzi Marco Bertolini Pietro Gentile Laura Verzellesi Valeria Trojani Luca De Simone Elena Bolletta Valentina Mastrofilippo Enrico Farnetti Davide Nicoli Stefania Croci Lucia Belloni Alessandro Zerbini Chantal Adani Michele De Maria Areti Kosmarikou Marco Vecchi Alessandro Invernizzi Fiorella Ilariucci Magda Zanelli Mauro Iori Luca Cimino |
| author_facet | Fabrizio Gozzi Marco Bertolini Pietro Gentile Laura Verzellesi Valeria Trojani Luca De Simone Elena Bolletta Valentina Mastrofilippo Enrico Farnetti Davide Nicoli Stefania Croci Lucia Belloni Alessandro Zerbini Chantal Adani Michele De Maria Areti Kosmarikou Marco Vecchi Alessandro Invernizzi Fiorella Ilariucci Magda Zanelli Mauro Iori Luca Cimino |
| author_sort | Fabrizio Gozzi |
| collection | DOAJ |
| description | Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL. |
| first_indexed | 2024-03-11T01:09:43Z |
| format | Article |
| id | doaj.art-6ea7407fb1464dd28a9ef8b45c366d7e |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T01:09:43Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-6ea7407fb1464dd28a9ef8b45c366d7e2023-11-18T18:59:02ZengMDPI AGDiagnostics2075-44182023-07-011314245110.3390/diagnostics13142451Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal LymphomaFabrizio Gozzi0Marco Bertolini1Pietro Gentile2Laura Verzellesi3Valeria Trojani4Luca De Simone5Elena Bolletta6Valentina Mastrofilippo7Enrico Farnetti8Davide Nicoli9Stefania Croci10Lucia Belloni11Alessandro Zerbini12Chantal Adani13Michele De Maria14Areti Kosmarikou15Marco Vecchi16Alessandro Invernizzi17Fiorella Ilariucci18Magda Zanelli19Mauro Iori20Luca Cimino21Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyMedical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOcular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyMedical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyMedical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOcular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOcular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOcular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyMolecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyMolecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyClinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyClinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyClinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOcular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOphthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOphthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOphthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyEye Clinic, Luigi Sacco Hospital, Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, ItalyHematology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalySurgical Oncology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, ItalyMedical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyOcular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, ItalyAnterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.https://www.mdpi.com/2075-4418/13/14/2451anterior segment optical coherence tomographyAS-OCTvitreoretinal lymphomaVRLradiomic featuresmachine learning |
| spellingShingle | Fabrizio Gozzi Marco Bertolini Pietro Gentile Laura Verzellesi Valeria Trojani Luca De Simone Elena Bolletta Valentina Mastrofilippo Enrico Farnetti Davide Nicoli Stefania Croci Lucia Belloni Alessandro Zerbini Chantal Adani Michele De Maria Areti Kosmarikou Marco Vecchi Alessandro Invernizzi Fiorella Ilariucci Magda Zanelli Mauro Iori Luca Cimino Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma Diagnostics anterior segment optical coherence tomography AS-OCT vitreoretinal lymphoma VRL radiomic features machine learning |
| title | Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma |
| title_full | Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma |
| title_fullStr | Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma |
| title_full_unstemmed | Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma |
| title_short | Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma |
| title_sort | artificial intelligence assisted processing of anterior segment oct images in the diagnosis of vitreoretinal lymphoma |
| topic | anterior segment optical coherence tomography AS-OCT vitreoretinal lymphoma VRL radiomic features machine learning |
| url | https://www.mdpi.com/2075-4418/13/14/2451 |
| work_keys_str_mv | AT fabriziogozzi artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT marcobertolini artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT pietrogentile artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT lauraverzellesi artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT valeriatrojani artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT lucadesimone artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT elenabolletta artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT valentinamastrofilippo artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT enricofarnetti artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT davidenicoli artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT stefaniacroci artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT luciabelloni artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT alessandrozerbini artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT chantaladani artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT micheledemaria artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT aretikosmarikou artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT marcovecchi artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT alessandroinvernizzi artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT fiorellailariucci artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT magdazanelli artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT mauroiori artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma AT lucacimino artificialintelligenceassistedprocessingofanteriorsegmentoctimagesinthediagnosisofvitreoretinallymphoma |