Radiomics
影像組學
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Introduction to Radiomics Learning
Introduction to Radiomics Learning
References: introduction
References: introduction
Lambin, Philippe, et al. "Radiomics: the bridge between medical imaging and personalized medicine." Nature Reviews Clinical Oncology 14.12 (2017): 749-762.
Gillies, Robert J., Paul E. Kinahan, and Hedvig Hricak. "Radiomics: images are more than pictures, they are data." Radiology 278.2 (2016): 563-577.
PyRadiomics: Van Griethuysen, Joost JM, et al. "Computational radiomics system to decode the radiographic phenotype." Cancer Research 77.21 (2017): e104-e107.
Radiomics Features and Analysis Methods
Radiomics Features and Analysis Methods
機器學習是實現人工智慧的重要手段之一，近期相當熱門的深度學習，也是屬於機器學習的一線分支，因此，了解機器學習的基本運作原理，是通往了解現代人工智慧的重要里程碑。在這一系列的影片中，我們會帶領大家認識機器學期的基本的概念與運作方式。同時，配合醫學題材，我們將會簡介一種抽取醫學影像特徵的方式：影像組學，並且透過實際的操作例子，示範如何結合機器學習與影像組學，產生可運作的人工智慧模型。
Speaker｜張大衛
國立臺灣大學・MeDA Lab
Lecture |
基礎機器學習（上）：機器學習簡介與流程 [35:00]
基礎機器學習（下）：模型訓練與過度擬合 [39:56]
影像組學簡介（精簡版） [40:21]
Hands-on |
機器學習與影像組學實作（上）：影像組學特徵抽取 [19:07]
機器學習與影像組學實作（下）：建立機器學習模型 [27:05]
影像組學 (radiomics) 是一種從影像中抽取特徵的方法，抽取出來的特徵可以像一般的數值統計資料一樣進行統計分析，又或是可用於機器學習訓練模型，達成利用數值資料來分析影像資料的目的。在這個系列的影片中，我們將介紹 (1) radiomics 的基本資訊、運用場合、實際的使用分析流程。(2) 特徵抽取的流程，以及流程中的各種變因與注意事項。(3) 形狀、強度，以及不同種類的材質特徵詳解。
Python Codes for Radiomics
Python Codes for Radiomics
Scientific Studies via Radiomics
Scientific Studies via Radiomics
References in Radiomics
References in Radiomics
Beginner
"Radiomics with artificial intelligence: a practical guide for beginners" by Burak Koçak, Emine Şebnem Durmaz, Ece Ateş, Özgür Kılıçkesmez. Diagn Interv Radiol 2019;25(6):485-495. https://dirjournal.org/articles/doi/dir.2019.19321
"Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features" by Damon Kim, Laura J Jensen, Thomas Elgeti, Ingo G Steffen, Bernd Hamm, Sebastian N Nagel. Tomography. 2021 Sep 17;7(3):477–487. https://www.mdpi.com/2379-139X/7/3/41
"Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies" by Maurizio Cè, Marius Dumitru Chiriac, Andrea Cozzi, Laura Macrì, Francesca Lucrezia Rabaiotti, Giovanni Irmici, Deborah Fazzini, Gianpaolo Carrafiello and Michaela Cellina. Diagnostics 2024, 14(22), 2473; https://doi.org/10.3390/diagnostics14222473
Intermediate
"Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study," by Laura J. Jensen, Damon Kim, Thomas Elgeti, Ingo G. Steffen, Lars-Arne Schaafs, Bernd Hamm & Sebastian N. Nagel. European Radiology Experimental volume 6, Article number: 43 (2022). https://eurradiolexp.springeropen.com/articles/10.1186/s41747-022-00297-7
Natally Horvat, Nikolaos Papanikolaou, Dow-Mu Koh. "Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use." Radiology: Artificial Intelligence, 2024. https://doi.org/10.1148/ryai.230437
Radiomics Feature Visualization. https://github.com/zhangjingcode/RadiomicsFeatureVisualization
[Radiomics feature extraction] "A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis" by Arthur, Amani, et al.. The Lancet Oncology 24.11 (2023): 1277-1286. https://doi.org/10.1016/S1470-2045(23)00462-X
[Subregion radiomics features] MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer." https://pubs.rsna.org/doi/10.1148/radiol.222830
[References: radiomics paper checklist] "CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII" by Burak Kocak et al. Insights into Imaging volume 14, Article number: 75 (2023). https://doi.org/10.1186%2Fs13244-023-01415-8
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