t1 colorectal carcinoma
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Cureus ◽  
2021 ◽  
Author(s):  
Naoki Asayama ◽  
Yukari Takeuchi ◽  
Kenjiro Shigita ◽  
Mayumi Kaneko ◽  
Shinji Nagata

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 506-506
Author(s):  
Yanlei Ma ◽  
Sheng Zhang ◽  
Xinxiang Li ◽  
Tianye Niu

506 Background: This study evaluates the predictive performance of radiomic features in metastasis of T1 colorectal carcinoma (CRC) to lymph nodes. Methods: A total of 10 200 CRC patients from our clinical cancer center included in this analysis. 225 eligible cases diagnosed with T1 CRC were included and divided into two groups: computed tomography (CT) image group (n = 82) and magnetic resonance image (MRI) group (n = 143) based on the preoperative image data available. A total of 548 radiomic features were extracted from each case and analyzed, and then a panel of radiomic features associated with lymph node metastases (LNM) were selected using Mann-Whitney U test. Combining these selected radiomic features and clinical data, the predictive performance for LNM was calculated using receiver operating characteristic (ROC) curves. Results: The prediction accuracy for LNM of T1 CRC could be improved to 0.88 by area under the receiver operating characteristic curve (AUC) through integration of one radiomic feature and three clinical indicators in CT group. In the group of contrast enhanced T1-weighted MRI (T1w-MRI), combination of two radiomic features and three clinical parameters present an AUC value of 0.85. In the group of T2-weighted MRI (T2w-MRI), combination of four radiomic features and five clinical characteristics identified T1 tumors with LNM with an AUC value of 0.87. Conclusions: The current study present a good predictive performance of combination of radiomic features with clinic characteristic in identifying T1 CRC with LNM, which may provide an important opportunity for us to make clinical treatment decision-making for T1 CRC patients.


2018 ◽  
Vol 06 (10) ◽  
pp. E1227-E1234 ◽  
Author(s):  
Krijn Haasnoot ◽  
Bas van der Spek ◽  
Christof Meischl ◽  
Dimitri Heine

Abstract Background and study aims Endoscopic full-thickness resection (eFTR) allows en-bloc and transmural resection of colorectal lesions for which other advanced endoscopic techniques are unsuitable. We present our experience with a novel “clip first, cut later” eFTR-device and evaluate its indications, efficacy and safety. Patients and methods From July 2015 through October 2017, 51 eFTR-procedures were performed in 48 patients. Technical success and R0-resection rates were prospectively recorded and retrospectively analyzed. Results Indications for eFTR were non-lifting adenoma (n = 19), primary resection of malignant lesion (n = 2), resection of scar tissue after incomplete endoscopic resection of low-risk T1 colorectal carcinoma (n = 26), adenoma involving a diverticulum (n = 2) and neuroendocrine tumor (n = 2). Two lesions were treated by combining endoscopic mucosal resection and eFTR. Technical success was achieved in 45 of 51 procedures (88 %). Histopathology confirmed full-thickness resection in 43 of 50 specimens (86 %) and radical resection (R0) in 40 procedures (80 %). eFTR-specimens, obtained for indeterminate previous T1 colorectal carcinoma resection, were free of residual carcinoma in 25 of 26 cases (96 %). In six patients (13 %) a total of eight adverse events occurred within 30 days after eFTR. One perforation occurred, which was corrected endoscopically. No emergency surgery was necessary. Conclusion In this study eFTR appears to be safe and effective for the resection of colorectal lesions. Technical success, R0-resection and major adverse events rate were reasonable and comparable with eFTR data reported elsewhere. Mean specimen diameter (23 mm) limits its use to relatively small lesions. A clinical algorithm for eFTR case selection is proposed. eFTR ensured local radical excision where other endoscopic techniques did not suffice and reduced the need for surgery in selected cases.


2018 ◽  
Vol 87 (6) ◽  
pp. AB492
Author(s):  
Ken Namikawa ◽  
Akiko Chino ◽  
Daisuke Ide ◽  
Shoichi Saito ◽  
Junko Fujisaki ◽  
...  

2018 ◽  
Author(s):  
Fumihiko Urushibara ◽  
Eisuke Shiozawa ◽  
Hideyuki Miyachi ◽  
Masashi Misawa ◽  
Tomonari Cho ◽  
...  

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