Application of the Node-RADS system for evaluation of regional lymph nodes based on computed tomography data in colon cancer patients

Introduction : Recent studies of neoadjuvant therapy for colon cancer indicate a significant percentage of patients’ retreatment, more likely due to the low accuracy of radiation diagnostics. The new Node-RADS lymph node assessment system can allow for more accurate selection of patients for neoadjuvant chemotherapy. Currently, the efficiency of adjuvant chemotherapy has been reliably substantiated for N1 within three months, for N2 within 6 months, for NO-only in the presence of negative prognosis factors. This allows us to study the applicability of neoadjuvant chemotherapy with high reliability of lymph node involvement according to computed tomography data in the preoperative period. Study objective : To determine the diagnostic efficiency of the Node-RADS system for predicting the risk of metastatic lymph node involvement in colon cancer patients. Materials and methods : A retrospective comparative analysis of computed tomography and pathomorphological examination data was performed for 75 colon cancer patients operated on at the M. N. A. S. Loginov in the period from 2021 to 2023. The clinical stage was routinely established based on the results of computed tomography of the chest and abdomen with intravenous contrast. Restaging was performed in the same patients according to the Node-RADS system with a lymph node score of 1-5 points. The results of two radiation diagnostic methods were compared with the data of the pathomorphological examination of the surgical material. Results: The accuracy (AUC) of computed tomography in the routine diagnostic method was 0.69. According to Node-RADS assessment, lymph nodes of 4-5 points were considered involved. Statistical analysis showed that the accuracy of this method increased significantly (AUC0.93). To determine the differences between the results of routine assessment, Node-RADS and the pathomorphological examination results, we found statistically significant (p < 0.0001) differences between the routine assessment and the results of pathomorphology, which corresponded to a low diagnostic value. At the same time, when comparing the results of the assessment according to Node-RADS and the conclusion of the pathomorphological study, no statistically significant differences were found (p = 0.109), which corresponded to sufficient diagnostic value of assessing the metastatic regional lymph nodes according to the Node-RADS criteria. Conclusions: The Node-RADS system, presented as a universal system for assessing malignant lymph nodes shows a fairly high diagnostic potential.

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Journal
Malignant tumours
Published
2024-12-17
DOI
https://doi.org/10.18027/2224-5057-2024-024
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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0.40
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article

Application of the Node-RADS system for evaluation of regional lymph nodes based on computed tomography data in colon cancer patients

Н. Н. Семенов, М. А. Данилов, О. С. Васнев, Yu. V. Kulezneva et al.
1 citations
Malignant tumours
Radiomics and Machine Learning in Medical Imaging
0.40
article

Application of the Node-RADS system for evaluation of regional lymph nodes based on computed tomography data in colon cancer patients

Н. Н. Семенов, М. А. Данилов, О. С. Васнев, Yu. V. Kulezneva, K. A. Lesko, M. A. Gorshkova, V. A. Prosikov
article en
1 citations

Abstract

Introduction : Recent studies of neoadjuvant therapy for colon cancer indicate a significant percentage of patients’ retreatment, more likely due to the low accuracy of radiation diagnostics. The new Node-RADS lymph node assessment system can allow for more accurate selection of patients for neoadjuvant chemotherapy. Currently, the efficiency of adjuvant chemotherapy has been reliably substantiated for N1 within three months, for N2 within 6 months, for NO-only in the presence of negative prognosis factors. This allows us to study the applicability of neoadjuvant chemotherapy with high reliability of lymph node involvement according to computed tomography data in the preoperative period. Study objective : To determine the diagnostic efficiency of the Node-RADS system for predicting the risk of metastatic lymph node involvement in colon cancer patients. Materials and methods : A retrospective comparative analysis of computed tomography and pathomorphological examination data was performed for 75 colon cancer patients operated on at the M. N. A. S. Loginov in the period from 2021 to 2023. The clinical stage was routinely established based on the results of computed tomography of the chest and abdomen with intravenous contrast. Restaging was performed in the same patients according to the Node-RADS system with a lymph node score of 1-5 points. The results of two radiation diagnostic methods were compared with the data of the pathomorphological examination of the surgical material. Results: The accuracy (AUC) of computed tomography in the routine diagnostic method was 0.69. According to Node-RADS assessment, lymph nodes of 4-5 points were considered involved. Statistical analysis showed that the accuracy of this method increased significantly (AUC0.93). To determine the differences between the results of routine assessment, Node-RADS and the pathomorphological examination results, we found statistically significant (p < 0.0001) differences between the routine assessment and the results of pathomorphology, which corresponded to a low diagnostic value. At the same time, when comparing the results of the assessment according to Node-RADS and the conclusion of the pathomorphological study, no statistically significant differences were found (p = 0.109), which corresponded to sufficient diagnostic value of assessing the metastatic regional lymph nodes according to the Node-RADS criteria. Conclusions: The Node-RADS system, presented as a universal system for assessing malignant lymph nodes shows a fairly high diagnostic potential.

Malignant tumoursVol. 14(3)
Moscow Clinical Scientific Center (RU), City Clinical Hospital No 31 (RU)
Openalex Percentile: Top 31%
Radiomics and Machine Learning in Medical Imaging
0.40
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