scholarly journals Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Zhou ◽  
Rui Yang ◽  
Yuan Wang ◽  
Meng Zhou ◽  
Xueyan Zhou ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Rectal Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Magnetic Resonance ◽  
Rectal Carcinoma ◽  
Roc Curve ◽  
Stepwise Logistic Regression ◽  
Resonance Imaging ◽  
T3 Rectal Cancer

Abstract Background Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma. Methods We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM. Results The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), T2WIKurtosis (p = 0.010), DWIMode (p = 0.038), DWICV (p = 0.038), and T2-mapP5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%. Conclusion The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer.

Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer with Magnetic Resonance Imaging

2017 ◽  
Vol 22 (1) ◽  
pp. 146-153 ◽  
Author(s):  
Jörn Gröne ◽  
Florian N. Loch ◽  
Matthias Taupitz ◽  
C. Schmidt ◽  
Martin E. Kreis
Keyword(s):  
Magnetic Resonance Imaging ◽  
Rectal Cancer ◽  
Lymph Node ◽  
Magnetic Resonance ◽  
Lymph Node Staging ◽  
Resonance Imaging

Accuracy of Different Criteria for Lymph Node Staging in Rectal Cancer by Magnetic Resonance Imaging

2017 ◽  
Vol 152 (5) ◽  
pp. S1232
Author(s):  
Florian Loch ◽  
Matthias Taupitz ◽  
Christoph Schmidt ◽  
Joern Groene ◽  
Martin E. Kreis
Keyword(s):  
Magnetic Resonance Imaging ◽  
Rectal Cancer ◽  
Lymph Node ◽  
Magnetic Resonance ◽  
Lymph Node Staging ◽  
Resonance Imaging

scholarly journals Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Xiangchun Liu ◽  
Qi Yang ◽  
Chunyu Zhang ◽  
Jianqing Sun ◽  
Kan He ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Rectal Cancer ◽  
Lymph Node ◽  
Magnetic Resonance ◽  
Clinical Data ◽  
Rectal Adenocarcinoma ◽  
Support Vector ◽  
Study Cohort ◽  
Resonance Imaging ◽  
Cohort Differences

ObjectiveTo develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.Methods186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Five support vector machine (SVM) classification models were built using selected clinical and semantic variables, single-regional radiomics features, multiregional radiomics features, and combinations, for predicting LN metastasis in rectal cancer. The performance of the five SVM models was evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing cohort. Differences in the AUCs among the five models were compared using DeLong’s test.ResultsThe clinical, single-regional radiomics and multiregional radiomics models showed moderate predictive performance and diagnostic accuracy in predicting LN metastasis with an AUC of 0.725, 0.702, and 0.736, respectively. A model with improved performance was created by combining clinical data with single-regional radiomics features (AUC = 0.827, (95% CI, 0.711–0.911), P = 0.016). Incorporating clinical data with multiregional radiomics features also improved the performance (AUC = 0.832 (95% CI, 0.717–0.915), P = 0.015).ConclusionMultiregional-based MRI radiomics combined with clinical data can improve efficacy in predicting LN metastasis and could be a useful tool to guide surgical decision-making in patients with rectal cancer.

The Therapeutical Importance of Endoscopic Pathology versus Magnetic Resonance Imaging findings for T1 Rectal Cancer: A Case Report

2016 ◽  
Author(s):  
Eiji Hidaka ◽  
Chiyo Maeda ◽  
Kenta Nakahara ◽  
Shoji Shimada ◽  
Fumio Ishida ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Rectal Cancer ◽  
Lymph Node ◽  
Magnetic Resonance ◽  
Lymph Nodes ◽  
Cancer Cells ◽  
Pelvic Lymph Node ◽  
Lower Rectal Cancer ◽  
Resonance Imaging ◽  
Lateral Pelvic Lymph Node

Abstract Introduction: Preoperative image-based diagnosis is important for the treatment of rare cases of T1 lower rectal cancers with lateral pelvic lymph node (LLN) metastasis. We report a case of LLN metastasis in T1 lower rectal cancer diagnosed preoperatively via magnetic resonance imaging (MRI). Case presentation: A 65-year-old woman was admitted to our hospital because of abdominal pain. An endoscopic examination revealed a large laterally spreading tumor in the lower rectum, which was en bloc resected using endoscopic submucosal dissection. Pathological examination of the resected specimen showed deep invasion of the cancer cells into the submucosal layer and lymphovascular invasion. MRI revealed swollen perirectal lymph nodes (â&#x89;¥5 mm) and a left LLN approximately 8 mm long. Laparoscopic abdominoperineal resection (Lap-APR) with left lateral pelvic lymph node dissection (LLND) was performed. Cancer cells were not seen in the resected material; however, 7 perirectal lymph nodes and 1 LLN of 47 lymph nodes contained metastatic cancer cells. Conclusion: We show that LLN metastasis in T1 lower rectal cancer can be preoperatively detected via MRI and successfully and safely treated via Lap-APR with left LLND.

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