大家好，这里是“跟着小蒋写SCI”，本专栏将针对影像组学SCI论文写作，推出系列文章，让新手宝宝们愉快地“抄作业”，不知不觉地在模仿中创新，顺利写出SCI。
上期推文中，我们推出了“四段论”的万能写作模板，依次是Introduction、Methods and materials、Results和Discussion。
有学员宝宝在后台抱怨：“Introduction不会写，Word打开又关上，一拖就是一周；一个月下来，挤牙膏似的挤出200个单词，怎么办？”
“这......那就不写了吧......”
“What?!”
“嘿嘿，我的意思是，先不写Introduction，而是从套路最固定的Methods and materials部分入手，正所谓‘柿子挑软的捏’嘛！”
话不多说，针对解螺旋推出的影像组学项目，小蒋已经整理好一套模板，现在就献给学员宝宝们享用。
直接看文献，第一篇《Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma》2020年发表在Frontiers in oncology （2021年IF 6.244）[1]：“A total of 516 lower-grade glioma (LGG) patients’ genomic data and clinical data were downloaded from the TCGA data portal [https://portal.gdc.cancer.gov/]. 
Among these 516 TCGA patients, 199 patients have MR images stored in the Cancer Imaging Archive (TCIA).
Additional genomic and clinical metadata of TCGA was obtained through cBioPortal. 
In addition, the genomics dataset of glioblastoma was also obtained from cBioPortal. 
All TCGA related data were previously anonymized and are publicly available.”
依次说明研究人群（LGG），基因组资料和临床资料从TCGA数据库下载（通过cBioPortal），MR影像从TCIA数据库下载；段末补充说明，所有数据均已匿名化；Figure2 展示纳排过程和结果。
第二篇《Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics》2021年发表在Neuro-Oncolgy（2021年IF 12.3）[2]：“The patient enrollment process is shown in Fig. 1. A total of 1202 patients who underwent preoperative MRI for newly diagnosed gliomas (grades II–IV) from January 2006 to June 2019 at Severance Hospital were considered for inclusion.
The inclusion criteria were as follows: (i) pathologically confirmed glioma, (ii) known IDH mutation status, (iii) preoperative MRI inclusive of postcontrast T1-weighted (T1C), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) images, and (iv) age≥18 years. The exclusion criteria were as follows: (i) history of biopsy or surgery for brain tumor (n=182), (ii) the absence of T1C, T2, or FLAIR images (n = 83), and/or (iii) unknown IDH status (n=81).
Therefore, a total of 856 patients were enrolled from Severance Hospital (Severance set). These patients were semi-randomly allocated into development (n = 727) and internal test sets (n = 129), with stratification for IDH status.
For external testing, a total of 203 patients from The Cancer Imaging Archive (TCIA set) were enrolled in accordance with the same criteria.”
依次介绍基于医疗机构的纳排标准和TCIA数据库的纳排标准，以及数据集的分配方法。
第三篇《Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction》2020年发表在Eur Radiol（2021年IF 5.315）[3]：“Along with IDH status, WHO grade and the clinical information including age, sex, resection extent, and overall survival. (OS) information were recorded.
Resection extent was categorized as subtotal (gross tumor removal ≥ 75% but < 100%), or partial (gross tumor removal, < 75%) or biopsy based on postoperative MRI findings. 
Details on IDH mutation status and clinical information are available in Table 1.”
介绍除了主变量（X）和因变量（Y），纳入的其他协变量（Z），以及部分分类变量的转换方式。
第四篇《A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study》2018年发表在Lancet Oncol（2021年IF 41.316）[4]：“Three separate cohorts were used to validate the radiomic signature. The TCGA dataset ...... was used to validate the concordance of the radiomic signature (from the MOSCATO training set) with the CD8 cell gene expression signature in this independent dataset. 
The immune phenotype dataset ...... was used to evaluate the concordance of the radiomic- signature with tumour immune phenotype of tumours.
The immunotherapy-treated cohort ...... was used to infer the association of the radiomic signature with clinical response in accordance with the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, progression-free survival, and overall survival. ”
依次阐述文章纳入的三个数据集的结局事件：CD8 表达水平、肿瘤免疫表型和免疫治疗效果（RECIST标准、PFS和OS）。
然后交待伦理和知情同意书：Our study was approved by the Gustave Roussy institutional review board and done in accordance with ethical standards of the 1964 Helsinki Declaration and its later amendments. 
Patients provided signed informed consent in accordance with their respective trial protocols.
解螺旋已上线影像组学项目，针对Methods and materials的第一“段落”，小蒋，整出“四句话”送给大家：
1）介绍研究人群
2）说明纳入的协变量
3）确定结局变量
4）豁免伦理
综合上述四篇文献如下：
Lower-grade glioma (LGG) patients’ genomic data and clinical data were downloaded from the TCGA data portal (https://portal.gdc.cancer.gov/).
 MR images were downloaded from TCIA. Patients underwent preoperative MRI for newly diagnosed gliomas from January 2006 to June 2019 were considered for inclusion. The inclusion criteria were as follows: (i) pathologically confirmed glioma, (ii) known IDH mutation status, (iii) preoperative MRI inclusive of postcontrast T1-weighted (T1C), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) images, and (iv) age≥18 years. The exclusion criteria were as follows: (i) history of biopsy or surgery for brain tumor (n=182), (ii) the absence of T1C, T2, or FLAIR images (n = 83), and/or (iii) unknown IDH status (n=81).
Along with IDH status, WHO grade and the clinical information including age, sex, resection extent, and overall survival. (OS) information were recorded.Resection extent was categorized as subtotal (gross tumor removal ≥ 75% but < 100%), or partial (gross tumor removal, < 75%) or biopsy based on postoperative MRI findings. 
TCGA, the immune phenotype dataset and the immunotherapy-treated cohort were used to validate the concordance of the radiomic signature with the CD8 cell gene expression signature, tumour immune phenotype of tumours and clinical response in accordance with PFS, respectively.
All TCGA and TCIA related data were previously anonymized and are publicly available. Our study was done in accordance with ethical standards of the 1964 Helsinki Declaration and its later amendments. Signed informed consent was waived.
好了，今天就唠到这吧~这里“跟着小蒋写SCI”专栏，每篇推文推进一个模块；如果有关于写作的问题以及文章中有不理解的地方欢迎在后台留言，拜了个拜！
参考文献
1. Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma. Front Oncol. 2020 Jun 26;PMID: 32676453;
2. Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics. Neuro Oncol. 2021 Feb 25;PMID: 32706862;
3. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction. Eur Radiol. 2020 Jul;PMID: 32162004；
4. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol.2018 Sep;PMID: 30120041.
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