Rényi Entropy-Based Shrinkage with RANSAC Refinement for Sparse Time-Frequency Distribution Reconstruction by Vedran Jurdana

“…The Rényi entropy-based two-step iterative shrinkage/thresholding (RTwIST) algorithm addresses this issue by incorporating local component estimates to guide adaptive thresholding, thereby improving interpretability and robustness. …”
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Comparative analysis of FISTA and inertial Tseng algorithm for enhanced image restoration in prostate cancer imaging by Abubakar Adamu, Huzaifa Umar, Samuel Eniola Akinade, Dilber Uzun Ozsahin

“…The Inertial Tseng Algorithm (ITA) and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) are well-established methods that provide effective ways to approximate zeros of the sum of monotone operators. …”
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Development of a Japanese polygenic risk score model for amyloid-β PET imaging in Alzheimer’s disease by Misato Kaishima, Junichi Ito, Kentaro Takahashi, Kenji Tai, Junro Kuromitsu, Shogyoku Bun, Daisuke Ito

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Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules by Xingxing Chen, Xingxing Chen, Lili Zhang, Bin Chen, Jiajia Lu

“…A total of 107 radiomics features were extracted from the US images, and the radiomics score (Rad-score) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. …”
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Multi‐sequence MRI‐based clinical‐radiomics models for the preoperative prediction of microsatellite instability‐high status in endometrial cancer by Zhuang Li, Yi Su, Yongbin Cui, Yong Yin, Zhenjiang Li

“…The intraclass correlation coefficients, Spearman correlation analysis, Mann–Whitney U test, and least absolute shrinkage and selection operator (LASSO) algorithm were employed for feature selection in radiomics models' development. …”
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MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS) by Youhao Yu, Richard M. Dansereau

“…We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. …”
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Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…Dimension reduction was conducted using variance threshold, univariate selection, and least absolute shrinkage and selection operator methods. …”
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A Robust Method Based on Deep Learning for Compressive Spectrum Sensing by Haoye Zeng, Yantao Yu, Guojin Liu, Yucheng Wu

“…To overcome these limitations, we propose BEISTA-Net, a deep learning-based framework for reconstructing compressed wideband signals. BEISTA-Net integrates the iterative shrinkage-thresholding algorithm (ISTA) with deep learning, thereby extracting and enhancing the block sparsity features of wideband spectrum signals, which significantly improves reconstruction accuracy. …”
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Prediction of a Panel of Programmed Cell Death Protein-1 (PD-1) Inhibitor–Sensitive Biomarkers Using Multiphase Computed Tomography Imaging Textural Features: Retrospective Cohort... by Shiqi Wang, Na Chai, Jingji Xu, Pengfei Yu, Luguang Huang, Quan Wang, Zhifeng Zhao, Bin Yang, Jiangpeng Wei, Xiangjie Wang, Gang Ji, Minwen Zheng

“…Decision curve analysis confirmed that the fused model provided higher clinical net benefit across threshold probabilities. ConclusionsThe construction of integrated biomarker prediction models through radiomics demonstrates technical feasibility, offering a promising methodology for comprehensive tumor characterization.…”
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Identifying low-risk breast cancer patients for axillary biopsy exemption: a multimodal preoperative predictive model by Jiaqi Zhang, Jianing Zhang, Zhihao Liu, Yudong Zhou, Xiaoni Zhao, Yalong Wang, Danni Li, Jinsui Du, Chenglong Duan, Yi Pan, Qi Tian, Feiqian Wang, Ke Wang, Lizhe Zhu, Bin Wang

“…The model achieved a maximum Youden index of 0.7789 with an optimal threshold of 0.3958. Conclusion Our multimodal predictive model integrates clinicopathological profiles with imaging biomarkers (ultrasound and magnetic resonance imaging). …”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…ObjectiveThis study aimed to develop a risk prediction model for post-treatment oligometastasis in nasopharyngeal carcinoma (NPC) by integrating pathomics features and an improved Support vector machine (SVM) algorithm, offering precise early decision support.MethodsThis study retrospectively included 462 NPC patients, without or with oligometastasis defined by ESTRO/EORTC criteria. …”
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Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employin... by Ming-Ya Luo, Yang Qu, Peng Zhang, Reziya Abuduxukuer, Li-Juan Wang, Li-Chong Yang, Zhi-Guo Li, Xiao-Dong Liu, Ce Han, Dan Li, Wei-Jia Wang, Dian-Ping Lv, Ming Liu, Jian Gao, Jing Xu, Yongfei Jiang, Hai-Nan Chen, Fu-Jin Li, Li-Ming Sun, Qi-Dong Sun, Yingbin Qi, Si-Yin Sun, Yu Zhang, Zhen-Ni Guo, Yi Yang

“…Thirty-three variables, including demographics, clinical data, and biomarkers (UCH-L1, S100β, NSE), were analyzed. Least Absolute Shrinkage and Selection Operator regression was used for feature selection, and six ML algorithms were tested. …”
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The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy by Mingyan Zhang, Junfa Sheng

“…Random forest algorithms were used to construct NR and SR models. …”
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