ABSTRACT
Many developing countries lack access to recommended first-line treatments for metastatic renal cell carcinoma (mRCC), such as immune checkpoint inhibitors (ICIs) or ICI-tyrosine kinase inhibitor (TKI) combinations. As a result, predictive markers are necessary to identify patients who may benefit from single-agent TKIs for long-term response. This study aims to identify such parameters. This was a multi-centre, retrospective study of patients with mRCC who were undergoing first-line treatment with sunitinib or pazopanib. Patients who had been diagnosed with mRCC and had not experienced disease progression for 36 months or more were deemed to have achieved a long-term response. Predictive clinical and pathological characteristics of patients who did not experience long-term disease progression were investigated. A total of 320 patients from four hospitals were included in the study. The median age of the patients was 60 years (range 20-89 years). According to the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk classification, 109 patients were classified as having favourable risk and 211 were in the intermediate-poor risk group. The median progression-free survival (PFS) and overall survival (OS) for all patients were 12.5 months and 76.4 months, respectively. In the long-term responder's group, the median PFS was 78.4 months. Among all patients, prior nephrectomy, the Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) <1, and the absence of brain metastasis were predictive factors for long-term response. For patients in the favourable risk group, the lack of brain metastasis was a predictor of long-term response. In the intermediate-poor risk group, prior nephrectomy and ECOG PS <1 were predictive factors for long-term response. Some individuals with mRCC may experience a durable response to TKIs. The likelihood of a long-term response can be determined by factors such as nephrectomy, ECOG PS < 1, and the absence of brain metastases.
ABSTRACT
INTRODUCTION: This study aimed to assess the role of the adjusted PNI-IMDC risk scoring system in stratifying the intermediate group of metastatic RCC patients who received TKIS in the first-line setting. METHODS: A total of 185 patients were included. The adjusted PNI and IMDC model was used to divide the intermediate group into two groups: intermediate PNI-high and intermediate PNI-low groups. The statistical data were analyzed using Kaplan-Meier and Cox regression analysis. RESULTS: The results showed that the adjusted PNI-IMDC risk score, classic IMDC, and PNI had similar prognostic values. Adjusted PNI-IMDC risk score might be used for a more homogeneous differentiation of the classic intermediate group. On the other hand, multivariate analysis revealed that the presence of nephrectomy, adjusted favorable/intermediate (PNI-high) group, ECOG performance score, and presence of bone metastasis were independent predictors of OS. CONCLUSIONS: Pre-treatment PNI, as a valuable and potential add-on biomarker to the adjusted PNI-IMDC classification model, can be helpful for establishing an improved prognostic model for intermediate group mRCC patients treated with first-line TKISs. Further validation studies are needed to clarify these findings.
ABSTRACT
An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The model makes use of joint time-frequency analysis (JTFA) for feature extraction and the artificial neural networks (ANN) for training and classification. Multiple ANNs are trained independently on a computer, using the backpropagation conjugate-gradient (CG) method, with input data collected from various flame and non-flame nuisance signals at four different IR wavelengths. The trained ANN connection weights are programmed into an embedded system as part of the filtering scheme for distinguishing flames from nuisance sources. Signal saturation caused by the excessive intensity of some IR sources is resolved by an adjustable gain control mechanism. The model described herein is employed in an industrial hydrocarbon flame detector.
