Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer
©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved..
BACKGROUND: Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task.
AIM: To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC.
METHODS: This study retrospectively enrolled 219 patients with RC [TDs+LNM- (n = 89); LNM+ TDs- (n = 115); TDs+LNM+ (n = 15)] from a single center between September 2016 and September 2021. Single-positive patients (i.e., TDs+LNM- and LNM+TDs-) were classified into the training (n = 163) and validation (n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients (i.e., TDs+LNM+) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm.
RESULTS: Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 (P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs+LNM+ (n = 15); single-positive (n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%).
CONCLUSION: Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
World journal of gastroenterology - 28(2022), 29 vom: 07. Aug., Seite 3960-3970 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Yong-Chang [VerfasserIn] |
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| Links: |
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| Themen: |
Biomarkers, Tumor |
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| Anmerkungen: |
Date Completed 03.04.2023 Date Revised 03.04.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.3748/wjg.v28.i29.3960 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM346737923 |
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| 100 | 1 | |a Zhang, Yong-Chang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer |
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| 500 | |a Date Revised 03.04.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task | ||
| 520 | |a AIM: To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC | ||
| 520 | |a METHODS: This study retrospectively enrolled 219 patients with RC [TDs+LNM- (n = 89); LNM+ TDs- (n = 115); TDs+LNM+ (n = 15)] from a single center between September 2016 and September 2021. Single-positive patients (i.e., TDs+LNM- and LNM+TDs-) were classified into the training (n = 163) and validation (n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients (i.e., TDs+LNM+) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm | ||
| 520 | |a RESULTS: Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 (P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs+LNM+ (n = 15); single-positive (n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%) | ||
| 520 | |a CONCLUSION: Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Differential diagnosis | |
| 650 | 4 | |a Lymph node metastasis | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Rectal cancer | |
| 650 | 4 | |a Tumor deposits | |
| 650 | 7 | |a Biomarkers, Tumor |2 NLM | |
| 700 | 1 | |a Li, Mou |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Yu-Mei |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Jing-Xu |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Chen-Cui |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Bin |e verfasserin |4 aut | |
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