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Bonanomi MTBC, de Almeida MTA, Hollaender MA, Bonanomi RC, Monteiro MLR. Retinoblastoma treatment in a Brazilian population. Presentation and long-term results. Cancer Med 2024; 13:e6683. [PMID: 38243643 PMCID: PMC10905530 DOI: 10.1002/cam4.6683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/17/2023] [Accepted: 10/23/2023] [Indexed: 01/21/2024] Open
Abstract
INTRODUCTION Retinoblastoma is a malignant tumor with a high cure potential when proper therapy is used. The purpose of this paper is to report the clinical features and outcomes of patients with retinoblastoma who were treated with a combination of local and systemic chemotherapy-based protocols. METHOD We retrospectively studied patients treated with systemic chemotherapy plus local treatment between 2003 and 2015 with a follow-up ≥2 years. We correlated clinical and pathological characteristics with decimal visual acuity (VA) and death. RESULTS Among 119 patients, 60% had unilateral disease (UNI), and 52% were male. The median presentation age was 19.5 months, 10% had a positive family history, and the most frequent sign was leukocoria (68.8%). Advanced disease was more frequent in eyes with UNI (98.4%) than in eyes with bilateral retinoblastoma (BIL: 55.3%). Enucleation was performed in 97% of UNI eyes and in 55.8% of BIL eyes. The overall globe salvage was 26.6%, 44.25% of BIL eyes. Bilateral enucleation was required in 5%. High-risk pathologic features occurred in 50% and 37% of eyes enucleated without and with neoadjuvant chemotherapy, respectively. High-risk features were related to the presence of goniosynechiae in the pathologic specimen and were more frequent in children younger than 10 months or older than 40 months. Extraocular disease was present in 5% of patients, and the death rate related to metastasis of the tumor was 8%. The final VA was ≥ 0.7 in 72.8% and ≥0.1 in 91% of BIL patients. CONCLUSIONS Treatment of retinoblastoma with conservative systemic-based chemotherapy was associated with an excellent survival rate (92%). Albeit the low overall globe salvage rate, in BIL patients, approximately half the eyes were conserved, and a satisfactory functional visual result was achieved The evaluated protocol is an important treatment option, especially in developing countries.
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Affiliation(s)
- Maria Teresa Brizzi Chizzotti Bonanomi
- Division of OphthalmologyHospital das Clínicas da Faculdade de Medicina da Universidade de São PauloSão PauloBrazil
- Laboratory for Investigation in Ophthalmology (LIM‐33), Faculdade de Medicina FMUSPUniversidade de São PauloSão PauloBrazil
| | - Maria Tereza A. de Almeida
- ITACI (Treatment of Children with Cancer Institute) and Children's InstituteHospital das Clínicas da Faculdade de Medicina da Universidade de São PauloSão PauloBrazil
| | - Marianna A. Hollaender
- Division of OphthalmologyHospital das Clínicas da Faculdade de Medicina da Universidade de São PauloSão PauloBrazil
| | - Roberta Chizzotti Bonanomi
- Division of OphthalmologyHospital das Clínicas da Faculdade de Medicina da Universidade de São PauloSão PauloBrazil
| | - Mario Luiz Ribeiro Monteiro
- Division of OphthalmologyHospital das Clínicas da Faculdade de Medicina da Universidade de São PauloSão PauloBrazil
- Laboratory for Investigation in Ophthalmology (LIM‐33), Faculdade de Medicina FMUSPUniversidade de São PauloSão PauloBrazil
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Zhang R, Dong L, Li R, Zhang K, Li Y, Zhao H, Shi J, Ge X, Xu X, Jiang L, Shi X, Zhang C, Zhou W, Xu L, Wu H, Li H, Yu C, Li J, Ma J, Wei W. Automatic retinoblastoma screening and surveillance using deep learning. Br J Cancer 2023; 129:466-474. [PMID: 37344582 PMCID: PMC10403507 DOI: 10.1038/s41416-023-02320-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND Retinoblastoma is the most common intraocular malignancy in childhood. With the advanced management strategy, the globe salvage and overall survival have significantly improved, which proposes subsequent challenges regarding long-term surveillance and offspring screening. This study aimed to apply a deep learning algorithm to reduce the burden of follow-up and offspring screening. METHODS This cohort study includes retinoblastoma patients who visited Beijing Tongren Hospital from March 2018 to January 2022 for deep learning algorism development. Clinical-suspected and treated retinoblastoma patients from February 2022 to June 2022 were prospectively collected for prospective validation. Images from the posterior pole and peripheral retina were collected, and reference standards were made according to the consensus of the multidisciplinary management team. A deep learning algorithm was trained to identify "normal fundus", "stable retinoblastoma" in which specific treatment is not required, and "active retinoblastoma" in which specific treatment is required. The performance of each classifier included sensitivity, specificity, accuracy, and cost-utility. RESULTS A total of 36,623 images were included for developing the Deep Learning Assistant for Retinoblastoma Monitoring (DLA-RB) algorithm. In internal fivefold cross-validation, DLA-RB achieved an area under curve (AUC) of 0.998 (95% confidence interval [CI] 0.986-1.000) in distinguishing normal fundus and active retinoblastoma, and 0.940 (95% CI 0.851-0.996) in distinguishing stable and active retinoblastoma. From February 2022 to June 2022, 139 eyes of 103 patients were prospectively collected. In identifying active retinoblastoma tumours from all clinical-suspected patients and active retinoblastoma from all treated retinoblastoma patients, the AUC of DLA-RB reached 0.991 (95% CI 0.970-1.000), and 0.962 (95% CI 0.915-1.000), respectively. The combination between ophthalmologists and DLA-RB significantly improved the accuracy of competent ophthalmologists and residents regarding both binary tasks. Cost-utility analysis revealed DLA-RB-based diagnosis mode is cost-effective in both retinoblastoma diagnosis and active retinoblastoma identification. CONCLUSIONS DLA-RB achieved high accuracy and sensitivity in identifying active retinoblastoma from the normal and stable retinoblastoma fundus. It can be used to surveil the activity of retinoblastoma during follow-up and screen high-risk offspring. Compared with referral procedures to ophthalmologic centres, DLA-RB-based screening and surveillance is cost-effective and can be incorporated within telemedicine programs. CLINICAL TRIAL REGISTRATION This study was registered on ClinicalTrials.gov (NCT05308043).
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Affiliation(s)
- Ruiheng Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ruyue Li
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Yitong Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Hongshu Zhao
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jitong Shi
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xin Ge
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xiaolin Xu
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Libin Jiang
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xuhan Shi
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chuan Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wenda Zhou
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Liangyuan Xu
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Haotian Wu
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Heyan Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chuyao Yu
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jing Li
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jianmin Ma
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
| | - Wenbin Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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Smriti V, Baheti AD, Shah S, Qureshi SS, Shetty N, Gala K, Kulkarni S, Raut A, Kamble V, Chinnaswamy G, Prasad M, C. P B, Ramadwar M, Singh S, Shukla A, Panwala H, Sahu A, Siddharth L, Kapadia T. Imaging Recommendations for Diagnosis, Staging, and Management of Pediatric Solid Tumors. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0042-1759507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
AbstractPaediatric extra-cranial solid tumours are one of the common causes for paediatric malignancies. Lack of appropriate imaging at presentation, staging and for follow-up is a major challenge for paediatric solid tumours. We have reviewed the paediatric solid tumour imaging protocols suggested by the major oncological societies/groups around the world (mainly the SIOP – Society International Pediatric Oncology, and the COG – Children's Oncology Group). We have adapted some of those protocols to develop imaging recommendations for the diagnosis, staging and management of extra-cranial solid tumours based on the treatment protocols followed in India.
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Affiliation(s)
- Vasundhara Smriti
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Akshay D. Baheti
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Sneha Shah
- Department of Nuclear Medicine and molecular imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Sajid S. Qureshi
- Division of Pediatric Surgical Oncology, Department of Surgical Oncology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Nanadan Shetty
- Department of Opthalmology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Kunal Gala
- Department of Intervention Radiology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Suyash Kulkarni
- Department of Intervention Radiology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Abhijit Raut
- Department of Radiodiagnosis, Kokilaben Dhirubhai Ambani Hospital, Mumbai, Maharashtra, India
| | - Veenita Kamble
- Department of Radiodiagnosis, Kokilaben Dhirubhai Ambani Hospital, Mumbai, Maharashtra, India
| | - Girish Chinnaswamy
- Department of Pediatric Oncology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Maya Prasad
- Department of Pediatric Oncology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Badira C. P
- Department of Pediatric Oncology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Mukta Ramadwar
- Department of Pathology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Suryaveer Singh
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Anuradha Shukla
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Hirenkumar Panwala
- Department of Radiodiagnosis, SRCC Children's Hospital, Mumbai, Maharashtra, India
| | - Arpita Sahu
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Laskar Siddharth
- Department of Radiation Oncology, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Tejas Kapadia
- Children's X-ray Department/Academic Unit of Paediatric Radiology, Royal Manchester Children's Hospital, Manchester, United Kingdom
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