Deep Learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

2021 ◽  
Author(s):  
Scarlet Brockmoeller ◽  
Amelie Echle ◽  
Narmin Ghaffari Laleh ◽  
Susanne Eiholm ◽  
Marie Louise Malmstrøm ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factor ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Colorectal Cancer ◽  
Node Metastasis

W1505: Value of 18F-FDG PET/CT in the Assessment of Lymph Node Metastasis With Early Colorectal Cancer

2010 ◽  
Vol 71 (5) ◽  
pp. AB345
Author(s):  
Si Hyung Lee ◽  
Kyeong Ok Kim ◽  
Byung-Ik Jang ◽  
Tae Nyeun Kim ◽  
Seongwoo Jeon ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Fdg Pet ◽  
Early Colorectal Cancer ◽  
Node Metastasis ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Predictors of lymph node metastasis and prognosis in early colorectal cancer patients with signet-ring cell and mucinous adenocarcinomas.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 717-717
Author(s):  
Baorong Song ◽  
Yongming Yu ◽  
Xiao Song ◽  
Xinxiang Li ◽  
Xiaoyu Dai ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Local Excision ◽  
Signet Ring Cell ◽  
Early Colorectal Cancer ◽  
Node Metastasis ◽  
Signet Ring ◽  
Ring Cell ◽  
Colorectal Adenocarcinomas

717 Background: The local excision of early colorectal cancer is limited by the presence of lymph node metastasis. Signet-ring cell carcinomas and mucinous adenocarcinomas are two relatively infrequent histological subtypes. However, little is known about the predictors of lymph node metastases and prognosis to support the feasibility of local excision in early-stage signet-ring cell and mucinous colorectal adenocarcinomas. Methods: The Surveillance Epidemiology and End Results Database (1988 to 2006) from the National Cancer Institute was used to identify all patients with T1 adenocarcinomas, including conventional adenocarcinoma (AC), mucinous adenocarcinomas (MAC), and signet-ring cell carcinoma (SRC). The prevalence of lymph node metastasis was assessed, and the long-term survival rate in the above three types of colorectal cancer was calculated. Results: Final cohorts of 47,260 patients were eligible for analysis. SRC accounted for 0.3% and MAC accounted for 3.7% of the entire cohort of colorectal adenocarcinomas. Compared to AC, SRC and MAC more frequently presented at a younger age, were located in the proximal colon, and exhibited a higher grade. The incidence of lymph node metastasis in AC, MAC, and SRC was 5.8%, 10.8%, and 15.3%, respectively. Patients with SRC were associated with a worse prognosis, regardless of the tumor location. Patients with MAC of the rectum, but not the colon, were associated with a reduced implication of prognosis. After adjusting for other clinicopathological factors, SRC and MAC of the rectum were independently associated with a high risk of death (HR 2.70, CI 1.77-4.12 and HR 1.65, CI 1.38-1.98 for SRC and MAC, respectively). Histology was not found to be an independent prognostic factor in patients with colon cancer. Conclusions: MAC and SRC are two distinct subtypes of colorectal cancer that require special attention despite their relatively rare prevalence. T1 patients with SRC and patients with MAC of the rectum have higher incidences of lymph node metastasis (LNM), and with these adverse outcomes, local excision is not recommended. Although MAC of the colon has a high rate of LNM, the prognosis of this type is similar to that of AC.

scholarly journals Downregulation of microRNA-100/microRNA-125b is associated with lymph node metastasis in early colorectal cancer with submucosal invasion

2017 ◽  
Vol 108 (3) ◽  
pp. 390-397 ◽  
Author(s):  
Yasuteru Fujino ◽  
Shunsaku Takeishi ◽  
Kensei Nishida ◽  
Koichi Okamoto ◽  
Naoki Muguruma ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Submucosal Invasion ◽  
Early Colorectal Cancer ◽  
Node Metastasis

scholarly journals Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sergei Bedrikovetski ◽  
Nagendra N. Dudi-Venkata ◽  
Hidde M. Kroon ◽  
Warren Seow ◽  
Ryash Vather ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Learning Models ◽  
Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

scholarly journals Different Machine Learning and Deep Learning Methods for the Classification of Colorectal Cancer Lymph Node Metastasis Images

2021 ◽  
Vol 8 ◽  
Author(s):  
Jin Li ◽  
Peng Wang ◽  
Yang Zhou ◽  
Hong Liang ◽  
Kuan Luan
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Transfer Learning ◽  
Raw Data ◽  
Node Metastasis ◽  
Treatment Plans

The classification of colorectal cancer (CRC) lymph node metastasis (LNM) is a vital clinical issue related to recurrence and design of treatment plans. However, it remains unclear which method is effective in automatically classifying CRC LNM. Hence, this study compared the performance of existing classification methods, i.e., machine learning, deep learning, and deep transfer learning, to identify the most effective method. A total of 3,364 samples (1,646 positive and 1,718 negative) from Harbin Medical University Cancer Hospital were collected. All patches were manually segmented by experienced radiologists, and the image size was based on the lesion to be intercepted. Two classes of global features and one class of local features were extracted from the patches. These features were used in eight machine learning algorithms, while the other models used raw data. Experiment results showed that deep transfer learning was the most effective method with an accuracy of 0.7583 and an area under the curve of 0.7941. Furthermore, to improve the interpretability of the results from the deep learning and deep transfer learning models, the classification heat-map features were used, which displayed the region of feature extraction by superposing with raw data. The research findings are expected to promote the use of effective methods in CRC LNM detection and hence facilitate the design of proper treatment plans.

Predicting lymph node metastasis in early colorectal cancer using the CITED1 expression

2013 ◽  
Vol 185 (1) ◽  
pp. 136-142 ◽  
Author(s):  
Toru Nasu ◽  
Yoshimasa Oku ◽  
Katsunari Takifuji ◽  
Tsukasa Hotta ◽  
Shozo Yokoyama ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Colorectal Cancer ◽  
Node Metastasis

Histopathological predictors of regional lymph node metastasis at the invasive front in early colorectal cancer

2011 ◽  
Vol 59 (3) ◽  
pp. 470-481 ◽  
Author(s):  
Yuri Akishima-Fukasawa ◽  
Yukio Ishikawa ◽  
Yoshikiyo Akasaka ◽  
Miwa Uzuki ◽  
Naomi Inomata ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Regional Lymph Node ◽  
Regional Lymph Node Metastasis ◽  
Early Colorectal Cancer ◽  
Invasive Front ◽  
Node Metastasis
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