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用于鉴别肝细胞癌和肝内胆管癌的连续时间随机游走直方图分析及受限谱成像

Histogram analysis of continuous-time random walk and restrictive spectrum imaging for identifying hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

作者信息

Dai Bo, Zhou Yihang, Shen Lei, Li Hanhan, Fang Ting, Pan Jiayin, Wang Yan, Mao Wei, Song Xiaopeng, Yan Fengshan, Wang Meiyun

机构信息

Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China.

Department of Radiology, Xinxiang Medical University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China.

出版信息

Front Oncol. 2025 Mar 10;15:1516995. doi: 10.3389/fonc.2025.1516995. eCollection 2025.

DOI:10.3389/fonc.2025.1516995
PMID:40134597
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11933651/
Abstract

BACKGROUND

To compare the ability and potential additional value of various diffusion models, including continuous-time random walk (CTRW), restrictive spectrum imaging (RSI), and diffusion-weighted imaging (DWI), as well as their associated histograms, in distinguishing the pathological subtypes of liver cancer.

METHODS

40 patients with liver cancer were included in this study. Histogram metrics were derived from CTRW (D, α, β), RSI (f, f, f), and DWI (ADC) parameters across the entire tumor volume. Statistical analyses included the Chi-square test, independent samples t-test, Mann-Whitney U test, ROC, logistic regression, and Spearman correlation.

RESULTS

Patients with hepatocellular carcinoma exhibited higher values in f, f, f, and f compared to patients with intrahepatic cholangiocarcinoma, whereas D, D, D, D, and D percentiles were lower (P<0.05). Among the individual histogram parameters, f percentile demonstrated the highest accuracy (AUC = 0.717). Regarding the combined and single models, the total combined model exhibited the best diagnostic performance (AUC = 0.792). Although RSI showed higher diagnostic efficacy than CTRW (AUC = 0.731, 0.717), the combination of CTRW and RSI further improved diagnostic performance (AUC = 0.787), achieving superior sensitivity and specificity (sensitivity = 0.72, specificity = 0.80).

CONCLUSION

CTRW, RSI, and their corresponding histogram parameters demonstrated the ability to distinguish between pathological subtypes of liver cancer. Moreover, whole-lesion histogram parameters provided more comprehensive statistical insights compared to mean values alone.

摘要

背景

比较包括连续时间随机游走(CTRW)、限制性谱成像(RSI)和扩散加权成像(DWI)及其相关直方图在内的各种扩散模型在区分肝癌病理亚型方面的能力和潜在附加价值。

方法

本研究纳入40例肝癌患者。通过整个肿瘤体积的CTRW(D、α、β)、RSI(f、f、f)和DWI(ADC)参数得出直方图指标。统计分析包括卡方检验、独立样本t检验、曼-惠特尼U检验、ROC、逻辑回归和斯皮尔曼相关性分析。

结果

与肝内胆管癌患者相比,肝细胞癌患者的f、f、f和f值更高,而D、D、D、D和D百分位数更低(P<0.05)。在各个直方图参数中,f百分位数显示出最高的准确性(AUC = 0.717)。关于联合模型和单一模型,总联合模型表现出最佳的诊断性能(AUC = 0.792)。虽然RSI显示出比CTRW更高的诊断效能(AUC = 0.731,0.717),但CTRW和RSI的联合进一步提高了诊断性能(AUC = 0.787),实现了更高的敏感性和特异性(敏感性 = 0.72,特异性 = 0.80)。

结论

CTRW、RSI及其相应的直方图参数显示出区分肝癌病理亚型的能力。此外,与单独的平均值相比,全病灶直方图参数提供了更全面的统计见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/17787f5c8b3c/fonc-15-1516995-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/379e2b8a598b/fonc-15-1516995-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/84109dc47f4e/fonc-15-1516995-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/0c3390bfa0be/fonc-15-1516995-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/f81b246c3648/fonc-15-1516995-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/17787f5c8b3c/fonc-15-1516995-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/379e2b8a598b/fonc-15-1516995-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/84109dc47f4e/fonc-15-1516995-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/0c3390bfa0be/fonc-15-1516995-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/f81b246c3648/fonc-15-1516995-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fdd/11933651/17787f5c8b3c/fonc-15-1516995-g005.jpg

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