ArticleAnnals of surgical oncology2020
Volumetric Histogram Analysis of Apparent Diffusion Coefficient as a Biomarker to Predict Survival of Esophageal Cancer Patients.
Article in Annals of surgical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study.European radiology experimental · 2026Article
- Machine learning-based clinical-radiomics model for predicting recurrence risk after radical surgery in sinonasal squamous cell carcinoma: a preliminary 2-year follow-up study.BMC medical imaging · 2025Article
- Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy.BMC cancer · 2025Article
- Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors.European journal of medical research · 2023Article
- Incremental prognostic value of ADC histogram analysis in patients with high-risk prostate cancer receiving adjuvant hormonal therapy after radical prostatectomy.Frontiers in oncology · 2023Article
- A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study.Frontiers in oncology · 2022Article
- Computed tomography-derived biomarker for predicting the treatment response to neoadjuvant chemoradiotherapy of rectal cancer.International journal of clinical oncology · 2021Article
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Authors and funding
10 authors.
Funding
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Abstract
backgroundThe purpose of this study was to investigate whether histogram analysis of an apparent diffusion coefficient (ADC) can serve as a prognostic biomarker for esophageal squamous cell carcinoma (ESCC).
methodsThis retrospective study enrolled 116 patients with ESCC who received curative surgery from 2006 to 2015 (including 70 patients who received neoadjuvant chemotherapy). Diffusion-weighted magnetic resonance imaging (DWI) was performed prior to treatment. The ADC maps were generated by DWIs at b = 0 and 1000 (s/mm
resultsKurtosis was significantly higher in tumors with lymphatic invasion (p = 0.005) with respect to the associations with pathological features. In univariate Cox regression analysis, tumor depth, lymph node status, mean ADC, and kurtosis were significantly correlated with RFS (p = 0.047, p < 0.001, p = 0.037, and p < 0.001, respectively), while lymph node status and kurtosis were also correlated with DSS (p = 0.002 and p = 0.017, respectively). Furthermore, multivariate analysis demonstrated that kurtosis was the independent prognostic factor for both RFS and DSS (p < 0.001 and p = 0.015, respectively). In Kaplan-Meier analysis, patients with higher kurtosis tumors (> 3.24) showed a significantly worse RFS and DFS (p < 0.001 and p = 0.006, respectively).
conclusionsHistogram analysis of ADC may serve as a useful biomarker for ESCC, reflecting pathological features and prognosis.
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