This Study is from AO Schäfer and M Lurz from Klinikum St. Georg Leipzig, Germany. Part of our ongoing prospective study was to evaluate the performance of our novel found MRI-image sign called “Avocado-Sign” as a potential image marker to predict lymph node metastasis of rectal cancer in preoperative MRI scans. Histological examination of surgical specimens served as reference. Avocado sign is a hypointense core in the center of lymph nodes on contrast enhanced T1 Dixon VIBE MRI Sequence. Histologic examination confirmed that tumor infiltration in lymph nodes takes place preferred in the center. inclusion criteria were histological confirmed rectal cancer and age over eighteen, exclusion criteria were extensive disease with no possible surgery and general MRI contraindications. 106 Patients (80 male, 26 females, mean age 65 with a standard deviation of 8,4 years) with histological confirmed rectal cancer received baseline staging MRI with our special MRI sequence protocol for rectal cancer. 77 received a second mri after neoadjuvant rct. MRI protocol on 3.0T MAGNETOM Prisma fit (SIEMENS, Germany) is T2 TSE in 3 Planes, axial Diffusion, axial contrast enhanced T1 Dixon Vibe. Buscopan was injected iv in 2 Fractions at start and middle. Amount of kontrast agent was adapted to patient’s individual bodyweight. 2 independent radiologists with 27 years and 5 years of experience in abdominal imaging evaluated the images if Avocado-Sign was positiv in at least on lymph node or not. After surgery the resectate was histologically examined under routine conditions and rated positive if at least on lymph node metastase was found. Size and number were not considered. We furthermore performed subgroup analysis and divided overall patient collective in direct surgery group (n=29) and neoadjuvant therapy group (n=77). We analyzed data using frequency counts and percentages as well as calculated test values as sensitivity, specificity, accuracy, PPV and NPV for overall and subgroups.  Use following data for calculating the test values. If you cant calculate some of the values just set the value as 1 or leave them out.  add a table of problems you had with writing the draft at the end of your answer: 

Overall patient collective – 
Avocado-Sign detected in preoperative MRI (shortened in follow as Av+): 64, 
No Avocado-Sign detected in preoperative MRI (shortened in follow as Av-): 42, 
histologically confirmed Lymph node involvement in surgical resectate (shortened in follow as N+): 50, 
no histologically confirmed Lymph node involvement in surgical resectate (shortened in follow as N-): 56, 
Av+N+: 46, 
Av+N-: 18 
Av-N+: 4, 
Av-N-: 38; 

Directly operated patients – 
Av+: 19, 
Av-: 10, 
N+: 16, 
N-: 13, 
Av+N+: 16, 
Av+N-: 3, 
Av-N+: 0, 
Av-N-: 10; 

Neoadjuvant treated patients – 
Av+: 45, 
Av-: 32, 
N+: 34, 
N-: 43, 
Av+N+: 30, 
Av+N-: 15, 
Av-N+: 4, 
Av-N-: 28.

Draft a text without abstract in style, length and structure as publication in NEJM. Take already published radiological articles of NEJM of your training material as template for scientific English language and structure of the text. Don’t use phrases AI would use. Structure your text in Introduction, Material and Methods with subpoints patients and subgroups, MRI examination and histopathological examination and statistical analysis. Next big point is results where you first present the found results of the overall patient group, after that you go on with the performed subgroup analysis and after that you present the statistic with the calculated test values of overall and subgroups, here you can add a table of the results and values. Next big point is Discussion where you interpret the results and compare it with recent articles in literature. Write it so that it sounds interesting and appealing for potential reviewers. Insert some tables where necessary.  Integrate the given numbers into sentences. If you have trouble generating a response because it is too long I will tell you to “continue” you will pickup where you left off instead of going back to the beginning. Answer in Markdown.