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classification and regression tree for estimating predictive markers to detect T790M mutations after acquired resistance to first line EGFR-TKI: HOPE-002

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INVESTIGATIONAL NEW DRUGS 2022年第2期40卷 361-369页

作者： Tamiya, Motohiro Fujikawa, Kei Suzuki, Hidekazu Yokoyama, Toshihide Uenami, Takeshi Tamiya, Akihiro Sato, Yuki Saito, Go Uchida, Junji Morita, Mitsunori Hirashima, Tomonori Fukuda, Yasushi Kanazu, Masaki Hosoya, Kazutaka Suzuki, Takuji Ueno, Kiyonobu Fujimoto, Daichi Kumagai, Toru Teramukai, Satoshi Osaka Int Ctr Inst Dept Thorac Oncol Osaka Japan Kyoto Prefectural Univ Med Grad Sch Med Sci Dept Biostat Kyoto Japan Osaka Habikino Med Ctr Dept Thorac Malignancy Osaka Japan Kurashiki Cent Hosp Dept Resp Med Okiyama Japan Natl Hosp Org Dept Thorac Oncol Osaka Toneyama Med Ctr Osaka Japan Natl Hosp Org Dept Internal Med Kinki Chuo Chest Med Ctr Osaka Japan Kobe City Med Ctr Gen Hosp Dept Resp Med Kobe Hyogo Japan Chiba Univ Grad Sch Med Dept Respirol Chiba Japan Osaka Gen Med Ctr Dept Resp Med Osaka Japan

Background and objective. Osimertinib as first-line treatment for patients with non-small cell lung cancer (NSCLC) harboring epidermal growth factor (EGFR) mutations remains controversial. Sequential EGFR-tyrosine kinase inhibitor (TKI) might be superior to the first line osimertinib in patients at risk of developing acquired T790M mutations. Methods. We enrolled consecutive patients with EGFR-mutated (deletion 19 or L858R) advanced NSCLC treated with first-line drugs and evaluated predictive markers using classification and regression tree (CART) for the detection of T790M mutations based on patient backgrounds prior to initial treatment. Results. Patients without acquired T790M mutations had worse outcomes than those with T790M mutations (median OS: 798 days vs. not reached;HR: 2.70;P < 0.001). CART identified three distinct groups based on variables associated with acquired T790M mutations (age, CYF, WBC, liver metastasis, and LDH;AUROC: 0.77). Based on certain variables, CART identified three distinct groups in deletion 19 (albumin, LDH, bone metastasis, pleural effusion, and WBC;AUROC: 0.81) and two distinct groups in L858R (age, CEA, and ALP;AUROC: 0.80). The T790M detection frequencies after TKI resistance of afatinib and first-generation EGFR-TKIs were similar (35.3% vs. 37.4%, P = 0.933). Afatinib demonstrated longer PFS (398 vs. 279 days;HR: 0.67;P = 0.004) and OS (1053 vs. 956 days;HR: 0.68;P = 0.051) than first-generation EGFR-TKIs. Conclusion. Identification of patients at risk of acquiring T790M mutations after EGFR-TKI failure may aid in choice of first-line EGFR-TKI. Furthermore, afatinib may be the more effective 1st-line EGFR-TKI treatment for patients at risk of developing T790M as initial EGFR-TKI resistance.

关键词： Non-small cell lung cancer EGFR Tyrosine kinase inhibitors T790M Predict marker classification and regression tree

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classification and regression tree (CART) model to assist clinical prediction for tracheostomy in patients with traumatic cervical spinal cord injury: a 7-year study of 340 patients

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EUROPEAN SPINE JOURNAL 2022年第5期31卷 1283-1290页

作者： Sun, Dawei Zhao, Hanqing Zhang, Zhengfeng Army Mil Med Univ Xinqiao Hosp Dept Orthoped 183 Xinqiao St Chongqing 400037 Peoples R China Xuzhou Med Univ Affiliated Huaihai Hosp Xuzhou Jiangsu Peoples R China

Objective To develop a classification and regression tree (CART) model to predict the need of tracheostomy in patients with traumatic cervical spinal cord injury (TCSCI) and to quantify scores of risk factors to make individualized clinical assessments. Methods The clinical characteristics of patients with TCSCI admitted to our hospital from January 2014 to December 2020 were retrospectively analyzed. The demographic characteristics (gender, age, smoking history), mechanism of injury, injury characteristics (ASIA impairment grades, neurological level of impairment, injury severity score), preexisting lung disease and preexisting medical conditions were statistically analyzed. The risk factors of tracheostomy were analyzed by univariate logistic regression analysis (ULRA) and multiple logistic regression analysis (MLRA). The CART model was established to predict tracheostomy. Results Three hundred and forty patients with TCSCI met the inclusion criteria, in which 41 patients underwent the tracheostomy. ULRA and MLRA showed that age > 50, ISS > 16, NLI > C5 and AIS A were significantly associated with tracheostomy. The CART model showed that AIS A and NLI > C5 were at the first and second decision node, which had a significant influence on the decision of tracheostomy. The final scores for tracheostomy from CART algorithm, composed of age, ISS, NLI and AIS A with a sensitivity of 0.78 and a specificity of 0.96, could also predict tracheostomy. Conclusion The establishment of CART model provided a certain clinical guidance for the prediction of tracheostomy in TCSCI. Quantifications of risk factors enable accurate prediction of individual patient risk of need for tracheostomy.

关键词： Cervical spinal cord injury Tracheostomy Trauma Risk factors classification and regression tree

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Infrared non-uniformity correction algorithm based on classification and regression tree segmentation

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OPTICAL ENGINEERING 2025年第1期64卷

作者： Qu, Wen-qing Ma, Hao-ran Wei, Jiang-yuan Ning, Yu Li, Jia-ming Ge, Kun Zhang, Hong-fei Wang, Jian Univ Sci & Technol China Dept Modern Phys State Key Lab Particle Detect & Elect Hefei Peoples R China Deep Space Explorat Lab Hefei Peoples R China Univ Sci & Technol China Sch Microelect Hefei Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei Peoples R China

In infrared imaging, pixel non-uniformity due to manufacturing and technological limitations significantly degrades image quality, posing a critical challenge for high-performance applications. This issue is especially pronounced in the field of infrared astronomical observation, where the scientific integrity of the data must be ensured when correcting infrared images. In addition, the coupling capacitance between pixels leads to nonlinear effects in pixel sensitivity. To address these challenges, a non-uniformity correction (NUC) algorithm was introduced that utilizes classification and regression tree segmentation. This approach enables precise corrections by adapting to varying illuminance levels, a capability not fully explored in existing solutions. In our method, we innovatively segment the pixel response curves into distinct low and high illuminance ranges and apply customized corrections for each segment to enhance the accuracy of correction. Evaluations using real image data demonstrate that our method enhances image quality and consistency. (c) 2025 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： infrared detector non-uniformity correction classification and regression tree

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Exploring Forest Soils Variability Based on Physicochemical Properties Using Correspondence Analysis and classification and regression tree for Forensic Provenance Analysis

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JOURNAL OF ANALYTICAL CHEMISTRY 2025年第1期80卷 182-194页

作者： Abd Hamid, Nadirah Baba, Muhamad Hilmi Sino, Hukil Halim, Azhar Abdul Lee, Loong Chuen Univ Kebangsaan Malaysia Fac Hlth Sci Forens Sci Program CODTIS Bangi 43600 Selangor Darul Malaysia Univ Kebangsaan Malaysia Fac Sci & Technol Dept Earth Sci & Environm Bangi 43600 Selangor Darul Malaysia

Soils on forest lands are likely characterized by high organic matter content and a diversity of nutrients. Thus, differentiating forest soils from different sites of origin is a challenging task. This study aims to assess the variability of 163 forest soils based on two easily measured physiochemical properties: soil color and pH. The soil samples were collected from 11 different sites across six states in Malaysia. Soil color was recorded under both air-dry and moistened conditions based on the Munsell Soil Color Chart. Meanwhile, soil pH was determined using a soil-to-water ratio of 1 : 20 (w/v). The association between the source origins and the physiochemical properties of the soils was assessed using simple correspondence analysis. classification of the soils by the type of anthropogenic activities, according to pH, was further modeled using the classification and regression tree algorithm. The results showed that soils from ecotourism sites tended to be less acidic than those from reserved forest sites. Meanwhile, soils from forest lands used for hiking showed acidity levels between the two types of sites. On the other hand, variability in soil color by the source origin was found to be insignificant. In conclusion, forest soils could be further classified according to the type of anthropogenic activities, which are partly governed by the type and magnitude of human activities.

关键词： forensic geoscience physiochemical properties Malaysian soil simple correspondence analysis classification and regression tree anthropogenic activities

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classification and regression tree (CART) for predicting cadmium (Cd) uptake by rice ( Oryza sativa L.) and its application to derive soil Cd threshold based on field data

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ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024年 285卷 117125页

作者： Tian, Haoting Zhang, Yan Yang, Xiaohui Zhang, Huan Wang, Dengfeng Wu, Pengbao Yin, Aijing Gao, Chao Suzhou Univ Sci & Technol Sch Environm Sci & Engn Suzhou 215009 Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing 210023 Peoples R China Nanjing Normal Univ Coll Marine Sci & Engn Nanjing 210023 Peoples R China Chinese Acad Trop Agr Sci Trop Crops Genet Resources Inst Haikou 571100 Hainan Peoples R China Huizhou Univ Sch Geog & Tourism Huizhou 516007 Guangdong Peoples R China Minist Ecol & Environm Nanjing Inst Environm Sci Nanjing 210042 Peoples R China

The entry of Cd into soil-rice systems is a growing concern as it can pose potential risks to public health. To derive regional soil Cd threshold, a total of 333 paired soil and rice samples was collected in Anhui Province, Eastern China. The results showed that the total soil Cd and soil Zn/Cd were the most significant variables contributing to Cd content in polished rice. The Chinese Soil Quality Standards might overestimate risk posed by Cd-contaminated soil for rice production in the mining area due to high Zn/Cd values of some mining-associated soils. Cd levels in polished rice can be predictable using stepwise multiple linear regression (MLR) model. However, the derived soil Cd threshold based on the MLR model would be unrealistically high. The classification and regression tree method (CART) performed well in simulating Cd levels in polished rice and can be used to derive soil Cd threshold instead of MLR to minimize the uncertainty.

关键词： Cadmium Rice Soil threshold classification and regression tree Field validation

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Dissecting Alzheimer's Disease Risk in Asian American Elders: A classification and regression tree Approach

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JOURNAL OF ALZHEIMERS DISEASE REPORTS 2024年第1期8卷 517-530页

作者： Moon, Sung Seek Anderson, Lindsey Li, Jinyu Lee, Jinwon Moon, Youngkwang Baylor Univ Diana R Garland Sch Social Work 811 Washington Ave Waco TX 76701 USA Creat Solut Healthcare Dallas TX USA

Background: Alzheimer's disease (AD) poses a growing public health challenge, particularly with an aging population. While extensive research has explored the relationships between AD, socio-demographic factors, and cardiovascular risk factors, a notable gap exists in understanding these connections within the Asian American elderly population. Objective: This study aims to address this gap by employing the classification and regressiontree (CART) approach to investigate the intricate interplay of socio-demographic variables, cardiovascular risk factors, sleep patterns, prior antidepressant use, and AD among Asian American elders. Methods: Data from the 2017 Uniform Data Set, provided by the National Alzheimer's Coordinating Center, were analyzed, focusing on a sample of Asian American elders (n = 4,343). The analysis utilized the classification and regression tree (CART) approach. Results: CART analysis identified critical factors, including levels of independence, specific age thresholds (73.5 and 84.5 years), apnea, antidepressant use, and body mass index, as significantly associated with AD risk. Conclusions: These findings have far-reaching implications for future research, particularly in examining the roles of gender, cultural nuances, socio-demographic factors, and cardiovascular risk elements in AD within the Asian American elderly population. Such insights can inform tailored interventions, improved healthcare access, and culturally sensitive policies to address the complex challenges posed by AD in this community.

关键词： Alzheimer's disease Asian American classification and regression tree National Alzheimer's Coordinating Center

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Prediction of anorectal malformations with bladder-neck/urethral-prostatic fistula using classification and regression tree analysis

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JOURNAL OF CLINICAL ULTRASOUND 2023年第3期51卷 436-444页

作者： Yuan, Peng Feng, Wei Huang, Yao Wang, Yi Chongqing Med Univ Childrens HospChongqing Key Lab Pediat Dept Neonatal SurgKey Lab Child Dev & Disorders Natl Clin Res Ctr Child Hlth & DisordersMinist E Chongqing Peoples R China Chongqing Med Univ Dept Neonatol Childrens Hosp Chongqing Peoples R China

Background The anorectal malformations (ARMs) with bladder-neck/urethral-prostatic fistula in males are the most complex groups in ARMs. It is essential to diagnose them effectively before the primary operation for both surgical modalities and future functional prognosis can be quite different from other types. Our study aimed to develop a classification and regression tree (CART) model for the prediction of ARMs with bladder-neck/urethral-prostatic fistula. Methods A total of 132 newborns with ARMs were included retrospectively and randomly assigned to the training sample and test sample in a 3:1 ratio. The pouch-perineum distance of ultrasound (PPDU), the pouch-perineum distance of invertogram (PPDI), and the rectum gas above the coccyx (RGAC) on the invertogram were hypothesized can serve as individual predictors. The CART analysis was used to determine the best combination of candidate predictors. The model's performance was assessed by the area under the receiver operating characteristic curve (AUC) and validated in the test sample. Results All three individual predictors were included in the CART model to predict the ARMs with bladder-neck/urethral-prostatic fistula in the derivation cohort with the following test characteristics (95% CI): sensitivity 75.6% (60.1 to 86.6);specificity 88.9% (76.7 to 95.4);AUC 0.909 (0.854 to 0.965). The model's predicted accuracy was validated in the test cohort (AUC = 0.883). In all 132 subjects, the AUC of the tree model was significantly superior to that of the best individual index: PPDU (0.901 vs. 0.819;p = 0.005). Conclusions A predictive model that consists of PPDU, PPDI, and RGAC may be useful in predicting ARMs with bladder-neck/urethral-prostatic fistula.

关键词： anorectal malformations classification and regression tree invertogram surgery ultrasound

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Differentiation of lacrimal gland tumors using the multi-model MRI: classification and regression tree (CART)-based analysis

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ACTA RADIOLOGICA 2022年第7期63卷 923-932页

作者： Li, Xiaofeng Wu, Xue Qian, Jiang Yuan, Yifei Wang, Shenjiang Ye, Xinpei Sha, Yan Zhang, Rui Ren, Hui Fudan Univ Dept Ophthalmol Eye & ENT Hosp 83 Fenyang Rd Shanghai 200031 Peoples R China Fudan Univ NHC Key Lab Myopia Shanghai Peoples R China Chinese Acad Med Sci Lab Myopia Shanghai Peoples R China Fudan Univ Dept Radiol Eye & ENT Hosp Shanghai PR Peoples R China

Background Little is known about the value of dynamic contrast-enhanced (DCE) in combination with diffusion-weighted imaging (DWI) for the differentiation of lacrimal gland tumors. Purpose To evaluate the ability of DCE and DWI in differentiating lacrimal gland tumors. Material and Methods DCE and DWI were performed in 72 patients with lacrimal gland tumors. Time-intensity curve (TIC) patterns were categorized as type A, type B, type C, and type D. Apparent diffusion coefficient (ADC) was measured on DWI. Then, the diagnostic effectiveness of TIC in conjunction with ADC was assessed using classification and regression tree (CART) analysis. Results Type A tumors were all epithelial;they could be further separated into pleomorphic adenoma sand carcinomas. Type B tumors were all non-epithelial tumors, which could be further separated into benign inflammatory infiltrates (BIIs) and lymphomas. Type C tumors contained both carcinomas and non-epithelial tumors, which could be diagnosed into carcinomas, BIIs and lymphomas. Type D tumors were all PAs. The mean ADC of epithelial tumors was significantly higher than that of non-epithelial tumors, and the mean ADC values were significantly different between PAs and carcinomas. Besides, the mean ADC value of BIIs was higher than that of lymphomas. Therefore, the CART decision tree made by ADC and TIC had a predictive accuracy of 86.1%, differentiating lacrimal gland tumors effectively. Conclusion Combined DCE and DWI-MRI can efficiently differentiate lacrimal gland tumors which can be of help to ophthalmologists in the diagnosis and treatment of these tumors.

关键词： Eye neoplasms lacrimal apparatus diseases dynamic contrast-enhanced diffusion-weighted imaging classification and regression tree CART

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Using classification and regression tree modeling to investigate appetite hormones and proinflammatory cytokines as biomarkers to differentiate bipolar I depression from major depressive disorder

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CNS SPECTRUMS 2022年第4期27卷 450-456页

作者： Huang, Kai-Lin Chen, Mu-Hong Hsu, Ju-Wei Tsai, Shih-Jen Bai, Ya-Mei Taipei Vet Gen Hosp Dept Psychiat Taipei Taiwan Natl Yang Ming Chiao Tung Univ Coll Med Dept Psychiat Taipei Taiwan

Background Altered immunity and metabolic profiles have been compared between bipolar depression (BD) and major depressive disorder (MDD). This study aimed at developing a composite predictor of appetite hormones and proinflammatory cytokines to differentiate BD from MDD. Methods This cross-sectional study enrolled patients with BD and those with MDD aged 20 to 59 years and displaying depressive episodes. Clinical characteristics (age, sex, body mass index, and depression severity), cytokines (C-reactive protein, interleukin [IL]-2, IL-6, tumor necrosis factor [TNF]-alpha, P-selectin, and monocyte chemoattractant protein), and appetite hormones (leptin, adiponectin, ghrelin, and insulin) were assessed as potential predictors using a classification and regression tree (CRT) model for differentiating BD from MDD. Results The predicted probability of a composite predictor of ghrelin and TNF-alpha was significantly greater (for BD: area under curve = 0.877;for MDD: area under curve = 0.914) than that of any one marker (all P > .05) to distinguish BD from MDD. The most powerful predictors for diagnosing BD were high ghrelin and TNF-alpha levels, whereas those for MDD were low ghrelin and TNF-alpha levels. Conclusion A composite predictor of ghrelin and TNF-alpha driven by CRT could assist in the differential diagnosis of BD from MDD with high specificity. Further clinical studies are warranted to validate our results and to explore underlying mechanisms.

关键词： Bipolar disorder major depressive disorder cytokines ghrelin classification and regression tree

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Predicting STEM Major Choice: a Machine Learning classification and regression tree Approach

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Journal for STEM Education Research 2023年第2期6卷 358-374页

作者： Chang, Chi-Ning Lin, Shuqiong Kwok, Oi-Man Saw, Guan Kung Virginia Commonwealth University Richmond VA United States American Institutes for Research Austin TX United States Texas A&M University College Station TX United States Claremont Graduate University Claremont CA United States

Despite the increasing demand for professionals in science, technology, engineering, and mathematics (STEM), only a small portion of young people in the USA pursue a postsecondary degree in STEM. To identify the major predictors of STEM participation, this study uses a machine learning approach, a classification and regression tree (CART), to analyze a wide range of individual, family, and school factors obtained from national survey data of US high school freshmen in fall 2009 who eventually enrolled in STEM college majors by 2016. The analytic results indicate that calculus credits, science identity, total STEM credits, and math achievement are the most predictive factors during the high school years of college STEM major selection. The CART-based tree also shows how these four variables interactively predict the likelihood of students enrolling in STEM college majors. © 2023, The Author(s).

关键词： classification and regression tree Course-taking High School Longitudinal Study of 2009–2016 Motivation STEM college major

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