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Shao L, Yang X, Sun Z, Tan X, Lu Z, Hu S, Dou W, Duan S. Three-dimensional pseudo-continuous arterial spin-labelled perfusion imaging for diagnosing upper cervical lymph node metastasis in patients with nasopharyngeal carcinoma: a whole-node histogram analysis. Clin Radiol 2024; 79:e736-e743. [PMID: 38341343 DOI: 10.1016/j.crad.2024.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024]
Abstract
AIM To evaluate whole-node histogram parameters of blood flow (BF) maps derived from three-dimensional pseudo-continuous arterial spin-labelled (3D pCASL) imaging in discriminating metastatic from benign upper cervical lymph nodes (UCLNs) for nasopharyngeal carcinoma (NPC) patients. MATERIALS AND METHODS Eighty NPC patients with a total of 170 histologically confirmed UCLNs (67 benign and 103 metastatic) were included retrospectively. Pre-treatment 3D pCASL imaging was performed and whole-node histogram analysis was then applied. Histogram parameters and morphological features, such as minimum axis diameter (MinAD), maximum axis diameter (MaxAD), and location of UCLNs, were assessed and compared between benign and metastatic lesions. Predictors were identified and further applied to establish a combined model by multivariate logistic regression in predicting the probability of metastatic UCLNs. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic performance. RESULTS Metastatic UCLNs had larger MinAD and MinAD/MaxAD ratio, greater energy and entropy values, and higher incidence of level II (upper jugular group), but lower BF10th value than benign nodes (all p<0.05). MinAD, BF10th, energy, and entropy were validated as independent predictors in diagnosing metastatic UCLNs. The combined model yielded an area under the curve (AUC) of 0.932, accuracy of 84.42 %, sensitivity of 80.6 %, and specificity of 90.29 %. CONCLUSIONS Whole-node histogram analysis on BF maps is a feasible tool to differentiate metastatic from benign UCLNs in NPC patients, and the combined model can further improve the diagnostic efficacy.
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Affiliation(s)
- L Shao
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - X Yang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - Z Sun
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China.
| | - X Tan
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - Z Lu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - S Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - W Dou
- General Electric (GE) Healthcare, MR Research China, Beijing, China
| | - S Duan
- General Electric (GE) Healthcare China, Shanghai, China
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Sieswerda M, van Rossum R, Bermejo I, Geleijnse G, Aben K, van Erning F, de Hingh I, Lemmens V, Dekker A, Verbeek X. Estimating Treatment Effect of Adjuvant Chemotherapy in Elderly Patients With Stage III Colon Cancer Using Bayesian Networks. JCO Clin Cancer Inform 2023; 7:e2300080. [PMID: 37748112 PMCID: PMC10569780 DOI: 10.1200/cci.23.00080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/31/2023] [Indexed: 09/27/2023] Open
Abstract
PURPOSE While adjuvant therapy with capecitabine and oxaliplatin (CAPOX) has been proven to be effective in stage III colon cancer, capecitabine monotherapy (CapMono) might be equally effective in elderly patients. Unfortunately, the elderly are under-represented in clinical trials and patients included may not be representative of the routine care population. Observational data might alleviate this problem but is sensitive to biases such as confounding by indication. Here, we build causal models using Bayesian Networks (BNs), identify confounders, and estimate the effect of adjuvant chemotherapy using survival analyses. METHODS Patients 70 years and older were selected from the Netherlands Cancer Registry (N = 982). We developed several BNs using constraint-based, score-based, and hybrid algorithms while precluding noncausal relations. In addition, we created models using a limited set of recurrence and survival nodes. Potential confounders were identified through the resulting graphs. Several Cox models were fitted correcting for confounders and for propensity scores. RESULTS When comparing adjuvant treatment with surgery only, pathological lymph node classification, physical status, and age were identified as potential confounders. Adjuvant treatment was significantly associated with survival in all Cox models, with hazard ratios between 0.39 and 0.45; CIs overlapped. BNs investigating CAPOX versus CapMono did not find any association between the treatment choice and survival and thus no confounders. Analyses using Cox models did not identify significant association either. CONCLUSION We were able to successfully leverage BN structure learning algorithms in conjunction with clinical knowledge to create causal models. While confounders differed depending on the algorithm and included nodes, results were not contradictory. We found a strong effect of adjuvant therapy on survival in our cohort. Additional oxaliplatin did not have a marked effect and should be avoided in elderly patients.
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Affiliation(s)
- Melle Sieswerda
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Ruby van Rossum
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Inigo Bermejo
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Gijs Geleijnse
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Katja Aben
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
- Department for Health Evidence, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Felice van Erning
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
- Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands
| | - Ignace de Hingh
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
- Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands
| | - Valery Lemmens
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - André Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Xander Verbeek
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
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