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Wibawa MS, Zhou JY, Wang R, Huang YY, Zhan Z, Chen X, Lv X, Young LS, Rajpoot N. AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma. Cancers (Basel) 2023; 15:5789. [PMID: 38136336 PMCID: PMC10742296 DOI: 10.3390/cancers15245789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/28/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Locoregional recurrence of nasopharyngeal carcinoma (NPC) occurs in 10% to 50% of cases following primary treatment. However, the current main prognostic markers for NPC, both stage and plasma Epstein-Barr virus DNA, are not sensitive to locoregional recurrence. METHODS We gathered 385 whole-slide images (WSIs) from haematoxylin and eosin (H&E)-stained NPC sections (n = 367 cases), which were collected from Sun Yat-sen University Cancer Centre. We developed a deep learning algorithm to detect tumour nuclei and lymphocyte nuclei in WSIs, followed by density-based clustering to quantify the tumour-infiltrating lymphocytes (TILs) into 12 scores. The Random Survival Forest model was then trained on the TILs to generate risk score. RESULTS Based on Kaplan-Meier analysis, the proposed methods were able to stratify low- and high-risk NPC cases in a validation set of locoregional recurrence with a statically significant result (p < 0.001). This finding was also found in distant metastasis-free survival (p < 0.001), progression-free survival (p < 0.001), and regional recurrence-free survival (p < 0.05). Furthermore, in both univariate analysis (HR: 1.58, CI: 1.13-2.19, p < 0.05) and multivariate analysis (HR:1.59, CI: 1.11-2.28, p < 0.05), we also found that our methods demonstrated a strong prognostic value for locoregional recurrence. CONCLUSION The proposed novel digital markers could potentially be utilised to assist treatment decisions in cases of NPC.
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Affiliation(s)
- Made Satria Wibawa
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; (M.S.W.); (R.W.)
| | - Jia-Yu Zhou
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Ruoyu Wang
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; (M.S.W.); (R.W.)
| | - Ying-Ying Huang
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zejiang Zhan
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Xi Chen
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Xing Lv
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Lawrence S. Young
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK;
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; (M.S.W.); (R.W.)
- The Alan Turing Institute, London NW1 2DB, UK
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Liandana M, Sabda Nirmala BM, Nanik Hendayanti NP, Satria Wibawa M, Bayu Krisna Putra DG. Purwarupa Smart Home menggunakan MQTT Broker untuk Memonitor Kondisi Abnormal Perangkat Listrik. Explore (NY) 2022. [DOI: 10.35200/explore.v12i1.545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Smart home telah berkembang pesat, salah satu fitur yang banyak dimiliki oleh sistem smart home adalah untuk melakukan pengontrolan dan monitoring perangkat listrik yang ada di rumah. Pengontrolan bertujuan untuk menyalakan atau mematikan perngkat listrik, sedangkan monitoring bertujuan untuk mengetahui kondisi dari perangkat listrik apakah sudah menyala atau belum. Selain untuk memonitoring perangkat listrik, fokus dari penelitian ini adalah untuk memonitoring perangkat listrik yang gagal menyala karena mengalami kerusakan atau perangkat terputus dengan sumber tegangannya, dalam penelitian ini disebut dengn istilah abnormal atau anomali. Purwarupa dari smart home yang telah dikembangkan menggunakan protokol MQTT, sensor arus ACS712, NodeMcu, dan aplikasi web. Purwarupa smarthome telah dapat melakukan monitoring dan pengontrolan perangkat listrik dan memberikan informasi jika perangkat gagal menyala. Waktu yang diperlukan untuk menyalakan atau mematikan perangkat listrik berkisar dari 1.4 detik hingga 1.5 detik.
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