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Hasnaoui A, Helal I, Ben Azouz Z, Hmidi A, Jouini R, Chadli-Debbiche A. A dataset of tumour-infiltrating lymphocytes in colorectal cancer patients using limited resources. Database (Oxford) 2023; 2023:0. [PMID: 38104276 PMCID: PMC10725308 DOI: 10.1093/database/baad089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/03/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
In the realm of cancer research, specifically focusing on colorectal carcinomas (CRCs), a novel diagnostic test referred to as 'Immunoscore' (IS) has emerged. This test relies on assessing the density of tumour-infiltrating lymphocytes, specifically CD3 and CD8, in both the centre of the tumour (CT) and its invasive margin (IM). IS holds promise as a potential prognostic factor. A retrospective descriptive study was conducted within the Pathology Department of Habib Thameur Hospital in Tunis, Tunisia. The study's aim was to evaluate the prognostic efficacy of IS for patients with CRC by means of a comprehensive survival analysis. This publication introduces the immunoscore in colorectal cancer (ISCRC) dataset, which was meticulously compiled during the aforementioned study. The ISCRC dataset comprises digital slide images sourced from biopsies of 104 patients diagnosed with CRC. Using the tissue microarray technique, an immunohistochemical investigation involving anti-CD3 and anti-CD8 markers was performed in regions designated as 'Hot Spots' within the CT and IM. The images were captured using a smartphone camera. Each marker's percentage presence within its respective region was quantified. The IS was estimated utilizing a semi-quantitative method. The ISCRC dataset encompasses anonymized personal data, along with macroscopic and microscopic attributes. The captured images, acquired through manual efforts using smartphones, stand as a valuable asset for the advancement of predictive algorithms Importantly, the potential applications of these models extend beyond mere prediction capabilities. They lay the groundwork for innovative mobile applications that could potentially revolutionize the practices of pathologists, particularly in healthcare settings constrained by resources and the absence of specialized scanning equipment. Database URL: https://figshare.com/s/5b4fa3e58c247a4851d4.
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Affiliation(s)
- Anis Hasnaoui
- Faculty of Medicine of Tunis, Tunis El Manar University, Rue Djebal Lakhdar, Tunis 1006, Tunisia
- Signals and Smart Systems Lab L3S, National Engineering School of Tunis, Tunis El Manar University, Campus Universitaire Farhat Hached B.P. n° 94 - ROMMANA, Tunis 1068, Tunisia
| | - Imen Helal
- Faculty of Medicine of Tunis, Tunis El Manar University, Rue Djebal Lakhdar, Tunis 1006, Tunisia
- Department of Pathology, Habib Thameur Hospital, Rue Ali Ben Ayed Montfleury, Tunis 1008, Tunisia
| | - Zouhour Ben Azouz
- Signals and Smart Systems Lab L3S, National Engineering School of Tunis, Tunis El Manar University, Campus Universitaire Farhat Hached B.P. n° 94 - ROMMANA, Tunis 1068, Tunisia
| | - Amira Hmidi
- Faculty of Medicine of Tunis, Tunis El Manar University, Rue Djebal Lakhdar, Tunis 1006, Tunisia
- Department of Pathology, Habib Thameur Hospital, Rue Ali Ben Ayed Montfleury, Tunis 1008, Tunisia
| | - Raja Jouini
- Faculty of Medicine of Tunis, Tunis El Manar University, Rue Djebal Lakhdar, Tunis 1006, Tunisia
- Department of Pathology, Habib Thameur Hospital, Rue Ali Ben Ayed Montfleury, Tunis 1008, Tunisia
| | - Aschraf Chadli-Debbiche
- Faculty of Medicine of Tunis, Tunis El Manar University, Rue Djebal Lakhdar, Tunis 1006, Tunisia
- Department of Pathology, Habib Thameur Hospital, Rue Ali Ben Ayed Montfleury, Tunis 1008, Tunisia
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