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  1. Prasad U, Wahid MI, Jalaludin MA, Abdullah BJ, Paramsothy M, Abdul-Kareem S
    Int J Radiat Oncol Biol Phys, 2002 Jul 1;53(3):648-55.
    PMID: 12062608
    To assess the long-term survival of patients with nasopharyngeal carcinoma (NPC) who were treated with conventional radical radiotherapy (RT) followed by adjuvant chemotherapy.
  2. Mohd Romlay MR, Mohd Ibrahim A, Toha SF, De Wilde P, Venkat I
    PLoS One, 2021;16(8):e0256665.
    PMID: 34432855 DOI: 10.1371/journal.pone.0256665
    Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.
  3. Ho GF, Chai CS, Alip A, Wahid MIA, Abdullah MM, Foo YC, et al.
    BMC Cancer, 2019 Sep 09;19(1):896.
    PMID: 31500587 DOI: 10.1186/s12885-019-6107-1
    BACKGROUND: This study aimed to evaluate the efficacy, side-effects and resistance mechanisms of first-line afatinib in a real-world setting.

    METHODS: This is a multicenter observational study of first-line afatinib in Malaysian patients with epidermal growth factor receptor (EGFR)-mutant advanced non-small cell lung cancer (NSCLC). Patients' demographic, clinical and treatment data, as well as resistance mechanisms to afatinib were retrospectively captured. The statistical methods included Chi-squared test and independent t-test for variables, Kaplan-Meier curve and log-rank test for survival, and Cox regression model for multivariate analysis.

    RESULTS: Eighty-five patients on first-line afatinib from 1st October 2014 to 30th April 2018 were eligible for the study. EGFR mutations detected in tumors included exon 19 deletion in 80.0%, exon 21 L858R point mutation in 12.9%, and rare or complex EGFR mutations in 7.1% of patients. Among these patients, 18.8% had Eastern Cooperative Oncology Group performance status of 2-4, 29.4% had symptomatic brain metastases and 17.6% had abnormal organ function. Afatinib 40 mg or 30 mg once daily were the most common starting and maintenance doses. Only one-tenth of patients experienced severe side-effects with none having grade 4 toxicities. The objective response rate was 76.5% while the disease control rate was 95.3%. At the time of analysis, 56 (65.9%) patients had progression of disease (PD) with a median progression-free survival (mPFS) of 14.2 months (95% CI, 11.85-16.55 months). Only 12.5% of the progressed patients developed new symptomatic brain metastases. The overall survival (OS) data was not mature. Thirty-three (38.8%) patients had died with a median OS of 28.9 months (95% CI, 19.82-37.99 months). The median follow-up period for the survivors was 20.0 months (95% CI, 17.49-22.51 months). Of patients with PD while on afatinib, 55.3% were investigated for resistance mechanisms with exon 20 T790 M mutation detected in 42.0% of them.

    CONCLUSIONS: Afatinib is an effective first-line treatment for patients with EGFR-mutant advanced NSCLC with a good response rate and long survival, even in patients with unfavorable clinical characteristics. The side-effects of afatinib were manageable and T790 M mutation was the most common resistance mechanism causing treatment failure.

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