4.5 Article

Preoperative prediction of intra-tumoral tertiary lymphoid structures based on CT in hepatocellular cancer

期刊

EUROPEAN JOURNAL OF RADIOLOGY
卷 151, 期 -, 页码 -

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.ejrad.2022.110309

关键词

Hepatocellular carcinoma; X-ray computed tomography; Tertiary lymphoid structures; Nomogram

资金

  1. National Key R&D Program of China [2021YFF1201003]
  2. Key-Area Research and Development Program of Guangdong Province [2021B0101420006]
  3. National Science Fund for Distinguished Young Scholars of China [81925023]
  4. National Science Foundation for Young Scientists of China [62102103, 82102145, 82102147]
  5. National Natural Science Foundation of China [82071892]
  6. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application [2022B1212010011]
  7. High-level Hospital Construction Project [DFJH201805, DFJHBF202105]
  8. Innovation Team of Kunming Medical University [CXTD202110]

向作者/读者索取更多资源

This study developed a nomogram for preoperative noninvasive prediction of intra-tumoral TLSs in patients with HCC using CT imaging features. The nomogram showed promising diagnostic performance and could be a useful tool in the clinical setting.
Purpose: Intra-tumoral tertiary lymphoid structures (TLSs) are associated with a favorable prognosis for patients with hepatocellular carcinoma (HCC). We aimed to identify image features related to TLSs and develop a nomogram for preoperative noninvasive prediction of intra-tumoral TLSs. Methods: This retrospective study enrolled patients with HCC who underwent contrast-enhanced computed to-mography before surgery between January 2014 and September 2020. Two radiologists retrospectively and independently reviewed the CT imaging features, and interobserver agreement was assessed. Univariable and multivariable logistic regression analyses were applied to investigate clinical laboratory data and imaging fea-tures related to TLSs. A regression-based predictive model and nomogram were constructed using the identified predictors. Nomogram diagnostic performance was assessed with the area under the receiver operating char-acteristic curve (AUC) and calibration curves, and validated using 5-fold cross-validation. Results: Ninety-three of the 142 HCCs were TLS + HCCs. Multivariable analyses identified intratumor arteries (odds ratio [OR]: 0.23; 95% confidence interval [CI]: 0.07-0.63; p = 0.007), intratumor hemorrhage (OR: 0.08; 95% CI: 0.01-0.50; p = 0.012), positive HBsAg or HCVAB status (OR: 4.52; 95% CI: 1.65-13.29; p = 0.004), platelet count (>= 186.5 x 10(9) /L, OR: 0.38; 95% CI: 0.16-0.86; p = 0.022), and aspartate transaminase level (>= 33.2 IU/l, OR: 0.24; 95% CI: 0.09-0.59; p = 0.003) as independent predictors of intra-tumoral TLSs. AUC of the regression-based model was 0.79 (95% CI:0.72-0.86) and average AUC at 5-fold cross-validation was 0.75 (95% CI: 0.71-0.80). Conclusions: CT-based nomogram is promising for preoperative prediction of intra-tumoral TLS in HCC.

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