Clinical Features, Non-Contrast CT Radiomic and Radiological Signs in Models for the Prediction of Hematoma Expansion in Intracerebral Hemorrhage

被引:2
|
作者
Chen, Zejia Frank [1 ]
Zhang, Liying [1 ]
Carrington, Andre M. [1 ,2 ,3 ]
Thornhill, Rebecca [2 ]
Miguel, Olivier [1 ,2 ]
Auriat, Angela M. [1 ,2 ]
Omid-Fard, Nima [2 ]
Hiremath, Shivaprakash [2 ]
Tshemeister Abitbul, Vered [1 ,2 ]
Dowlatshahi, Dar [1 ,4 ]
Demchuk, Andrew [5 ]
Gladstone, David [6 ]
Morotti, Andrea [7 ]
Casetta, Ilaria [8 ]
Fainardi, Enrico [9 ]
Huynh, Thien [10 ,11 ]
Elkabouli, Marah [1 ]
Talbot, Zoe [1 ]
Melkus, Gerd [1 ,2 ]
Aviv, Richard, I [1 ,2 ,12 ]
机构
[1] Ottawa Hosp Res Inst, Ottawa, ON, Canada
[2] Univ Ottawa, Dept Radiol Radiat Oncol & Med Phys, Ottawa, ON, Canada
[3] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON, Canada
[4] Univ Ottawa, Dept Med Neurol, Ottawa, ON, Canada
[5] Foothills Med Ctr, Dept Med Neurol, Calgary, AB, Canada
[6] Univ Toronto, Sunnybrook Hlth Sci Ctr, Dept Med Neurol, Toronto, ON, Canada
[7] ASST Spedali Civili Brescia, Dept Neurol Sci & Vis, Neurol Unit, Brescia, Italy
[8] Univ Ferrara, Neurol Clin, Ferrara, Italy
[9] Univ Florence, Dept Expt & Clin Biomed Sci, Neuroradiol Unit, Florence, Italy
[10] Mayo Clin, Dept Radiol, Jacksonville, FL USA
[11] Mayo Clin, Dept Neurosurg, Jacksonville, FL USA
[12] Univ Ottawa, Ottawa Hosp, Dept Radiol, Gen Campus,CPCR Bldg,Room L2121, Ottawa, ON K1H 1M2, Canada
来源
CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES | 2023年 / 74卷 / 04期
基金
加拿大创新基金会;
关键词
hematoma expansion; intracerebral hemorrhage; radiomics; machine learning; non-contrast CT; NONCONTRAST COMPUTED-TOMOGRAPHY; GROWTH; SCORES;
D O I
10.1177/08465371231168383
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeRapid identification of hematoma expansion (HE) risk at baseline is a priority in intracerebral hemorrhage (ICH) patients and may impact clinical decision making. Predictive scores using clinical features and Non-Contract Computed Tomography (NCCT)-based features exist, however, the extent to which each feature set contributes to identification is limited. This paper aims to investigate the relative value of clinical, radiological, and radiomics features in HE prediction.MethodsOriginal data was retrospectively obtained from three major prospective clinical trials ["Spot Sign" Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy (SPOTLIGHT)NCT01359202; The Spot Sign for Predicting and Treating ICH Growth Study (STOP-IT)NCT00810888] Patients baseline and follow-up scans following ICH were included. Clinical, NCCT radiological, and radiomics features were extracted, and multivariate modeling was conducted on each feature set.Results317 patients from 38 sites met inclusion criteria. Warfarin use (p=0.001) and GCS score (p=0.046) were significant clinical predictors of HE. The best performing model for HE prediction included clinical, radiological, and radiomic features with an area under the curve (AUC) of 87.7%. NCCT radiological features improved upon clinical benchmark model AUC by 6.5% and a clinical & radiomic combination model by 6.4%. Addition of radiomics features improved goodness of fit of both clinical (p=0.012) and clinical & NCCT radiological (p=0.007) models, with marginal improvements on AUC. Inclusion of NCCT radiological signs was best for ruling out HE whereas the radiomic features were best for ruling in HE.ConclusionNCCT-based radiological and radiomics features can improve HE prediction when added to clinical features. Visual Abstract Objectif : L'identification rapide du risque d'expansion d'un hematome avant le debut des traitements est une priorite chez les patients ayant une hemorragie intracerebrale (HI) et elle peut avoir des consequences sur la prise de decisions cliniques. Il existe des scores predictifs utilisant les caracteristiques cliniques et les caracteristiques basees sur la tomodensitometrie sans contraste (TDM-sc). Toutefois, la portee de la contribution a l'identification de chaque ensemble de caracteristiques est limitee. Cet article vise a etudier la valeur relative des caracteristiques cliniques, radiologiques et radiomiques pour la prediction de l'expansion des hematomes. Methodes : Les donnees originales ont ete obtenues retrospectivement a partir de deux etudes cliniques prospectives majeures Spotlight et Spot-ITrr; : << Spot Sign >> Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy (SPOTLIGHT) (NCT01359202); The Spot Sign for Predicting and Treating ICH Growth Study (STOP-IT) (NCT00810888). Des examens d'imagerie de patients ayant souffert d'une HI effectues avant les traitements et au cours du suivi ont ete inclus. Les caracteristiques cliniques, radiologiques (TDM-sc) et radiomiques ont ete extraites et une modelisation multifactorielle a ete effectuee sur chaque ensemble de caracteristiques. Resultats : Une population de 317 patients provenant de 38 centres satisfaisait les criteres d'inclusion. L'utilisation de la warfarine (P = 0.001) et le score GCS (P = 0.046) ont ete des facteurs cliniques predictifs de l'expansion de l'hematome. Le modele le plus performant pour la prediction de l'expansion de l'hematome incluait les caracteristiques cliniques, radiologiques et radiomiques et presentait une aire sous la courbe (ASC) de 87.7 %. Les caracteristiques radiologiques (TDM-sc) ont ameliore de 6.5 % l'ASC du modele de reference clinique et de 6.4 % celle d'un modele combinant clinique et radiomique. L'ajout des caracteristiques radiomiques a ameliore la qualite d'adaptation des modeles cliniques et radiologiques (TDM-sc) avec une amelioration marginale de l'ASC. L'inclusion des signes radiologiques (TDM-sc) a ete le meilleur moyen d'ecarter un diagnostic d'hematome, tandis que les caracteristiques cliniques ont ete les meilleures pour confirmer son existence. Conclusions : Les caracteristiques radiologiques basees sur une TDM sans contraste et les caracteristiques radiomiques peuvent ameliorer la prediction de l'expansion d'un hematome intracerebral quand elles sont ajoutees aux caracteristiques cliniques.
引用
收藏
页码:713 / 722
页数:10
相关论文
共 50 条
  • [41] Increased Prognostic Yield by Combined Assessment of Non-Contrast Computed Tomography Markers of Antithrombotic-Related Spontaneous Intracerebral Hemorrhage Expansion
    Katsanos, Aristeidis H.
    Gupta, Himanshu
    Morotti, Andrea
    Beshara, Simon
    Patil, Tushar
    Al-Zahrani, Saeed
    Tsivgoulis, Georgios
    Dowlatshahi, Dariush
    Goldstein, Joshua N.
    Charidimou, Andreas
    Shoamanesh, Ashkan
    JOURNAL OF CLINICAL MEDICINE, 2022, 11 (06)
  • [42] Risk Prediction of Cardioembolic Stroke using Clinical Data and Non-contrast CT
    Jakkrawankul, Pasit
    Chunharas, Chaipat
    Akarathanawat, Wasan
    Vorasayan, Pongpat
    Chunamchai, Sedthapong
    Pratanwanich, Ploy N.
    Punyabukkana, Proadpran
    Chuangsuwanich, Ekapol
    2023 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP, SSP, 2023, : 433 - 437
  • [43] Noncontrast Computed Tomography Signs as Predictors of Hematoma Expansion, Clinical Outcome, and Response to Tranexamic Acid in Acute Intracerebral Hemorrhage
    Law, Zhe Kang
    Ali, Azlinawati
    Krishnan, Kailash
    Bischoff, Adam
    Appleton, Jason P.
    Scutt, Polly
    Woodhouse, Lisa
    Pszczolkowski, Stefan
    Cala, Lesley A.
    Dineen, Robert A.
    England, Timothy J.
    Ozturk, Serefnur
    Roffe, Christine
    Bereczki, Daniel
    Ciccone, Alfonso
    Christensen, Hanne
    Ovesen, Christian
    Bath, Philip M.
    Sprigg, Nikola
    STROKE, 2020, 51 (01) : 121 - 128
  • [44] Multi-institutional external validation of automated segmentation models for intracerebral hemorrhage (ICH) and Perihematomal edema (PHE) on Non-Contrast Head CT
    Payabvash, Sam
    Desser, Dmitriy
    Nawabi, Jawed
    Abou Karam, Gaby
    Zeevi, Tal
    Dierksen, Fiona
    Qureshi, Adnan I.
    Sanelli, Pina C.
    Werring, David J.
    Malhotra, Ajay
    De Havenon, Adam
    Falcone, Guido J.
    Sheth, Kevin N.
    CEREBROVASCULAR DISEASES, 2023, 52 : 71 - 71
  • [45] Joint Segmentation of Intracerebral Hemorrhage and Infarct from Non-Contrast CT Images of Post-treatment Acute Ischemic Stroke Patients
    Kuang, Hulin
    Najm, Mohamed
    Menon, Bijoy K.
    Qiu, Wu
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, PT III, 2018, 11072 : 681 - 688
  • [46] In spontaneous intracerebral hematoma patients, prediction of the hematoma expansion risk and mortality risk using radiological and clinical markers and a newly developed scale
    Bakar, Bulent
    Akkaya, Suleyman
    Say, Bahar
    Yuksel, Ulas
    Alhan, Aslihan
    Turgut, Esra
    Ogden, Mustafa
    Ergun, Ufuk
    NEUROLOGICAL RESEARCH, 2021, 43 (06) : 482 - 495
  • [47] Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography
    Feng, Changfeng
    Ding, Zhongxiang
    Lao, Qun
    Zhen, Tao
    Ruan, Mei
    Han, Jing
    He, Linyang
    Shen, Qijun
    EUROPEAN RADIOLOGY, 2024, 34 (05) : 2908 - 2920
  • [48] Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
    Tran, Anh T.
    Zeevi, Tal
    Haider, Stefan P.
    Abou Karam, Gaby
    Berson, Elisa R.
    Tharmaseelan, Hishan
    Qureshi, Adnan I.
    Sanelli, Pina C.
    Werring, David J.
    Malhotra, Ajay
    Petersen, Nils H.
    de Havenon, Adam
    Falcone, Guido J.
    Sheth, Kevin N.
    Payabvash, Seyedmehdi
    NPJ DIGITAL MEDICINE, 2024, 7 (01)
  • [49] Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage
    Weixiong Zeng
    Jiaying Chen
    Linling Shen
    Genghong Xia
    Jiahui Xie
    Shuqiong Zheng
    Zilong He
    Limei Deng
    Yaya Guo
    Jingjing Yang
    Yijun Lv
    Genggeng Qin
    Weiguo Chen
    Jia Yin
    Qiheng Wu
    BMC Medical Imaging, 25 (1)
  • [50] Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage
    Lingxu Chen
    Xiaochen Wang
    Sihui Wang
    Xuening Zhao
    Ying Yan
    Mengyuan Yuan
    Shengjun Sun
    BMC Medical Imaging, 25 (1)