Top

Insights into Imaging

Published in:

Open Access 01-12-2023 | Gastric Cancer | Original Article

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

Authors: Yongjian Zhu, Peng Wang, Bingzhi Wang, Zhichao Jiang, Ying Li, Jun Jiang, Yuxin Zhong, Liyan Xue, Liming Jiang

Published in: Insights into Imaging | Issue 1/2023

Abstract

Objective

To construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis.

Methods

A total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (C_DLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and C_DLCT. The Kaplan–Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model.

Results

In this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010).

Conclusion

The combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery.

Critical relevance statement

MSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC.

Key points

• Tumor location and CT_N staging were independent predictors for MSI-H in GC.

• Quantitative DLCT parameters showed potential in predicting MSI status in GC.

• The combined model integrating clinico-radiologic features and C_DLCT could improve the predictive performance.

• The prediction results could stratify the risk of tumor recurrence after surgery.

Graphical Abstract

Available only for authorised users

Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660CrossRefPubMed

Zheng R, Zhang S, Zeng H et al (2022) Cancer incidence and mortality in China, 2016. J Natl Cancer Center 2:1–9. https://doi.org/10.1016/j.jncc.2022.02.002CrossRef

Zeng H, Chen W, Zheng R et al (2018) Changing cancer survival in China during 2003–15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health 6:e555–e567. https://doi.org/10.1016/S2214-109X(18)30127-XCrossRefPubMed

Cancer Genome Atlas Research Network (2014) Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513:202–209. https://doi.org/10.1038/nature13480CrossRef

Cristescu R, Lee J, Nebozhyn M et al (2015) Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med 21:449–456. https://doi.org/10.1038/nm.3850CrossRefPubMed

Puliga E, Corso S, Pietrantonio F, Giordano S (2021) Microsatellite instability in gastric cancer: between lights and shadows. Cancer Treat Rev 95:102175. https://doi.org/10.1016/j.ctrv.2021.102175CrossRefPubMed

Ratti M, Lampis A, Hahne JC, Passalacqua R, Valeri N (2018) Microsatellite instability in gastric cancer: molecular bases, clinical perspectives, and new treatment approaches. Cell Mol Life Sci 75:4151–4162. https://doi.org/10.1007/s00018-018-2906-9CrossRefPubMedPubMedCentral

Zubarayev M, Min EK, Son T (2019) Clinical and molecular prognostic markers of survival after surgery for gastric cancer: tumor-node-metastasis staging system and beyond. Transl Gastroenterol Hepatol 4:59. https://doi.org/10.21037/tgh.2019.08.05CrossRefPubMedPubMedCentral

Chao J, Fuchs CS, Shitara K et al (2021) Assessment of pembrolizumab therapy for the treatment of microsatellite instability-high gastric or gastroesophageal junction cancer among patients in the KEYNOTE-059, KEYNOTE-061, and KEYNOTE-062 clinical trials. JAMA Oncol 7:895–902. https://doi.org/10.1001/jamaoncol.2021.0275CrossRefPubMedPubMedCentral

10.

Joshi SS, Badgwell BD (2021) Current treatment and recent progress in gastric cancer. CA Cancer J Clin 71:264–279. https://doi.org/10.3322/caac.21657CrossRefPubMedPubMedCentral

11.

Ramos MFKP, Pereira MA, Amorim LC et al (2020) Gastric cancer molecular classification and adjuvant therapy: is there a different benefit according to the subtype? J Surg Oncol 121:804–813. https://doi.org/10.1002/jso.25792CrossRefPubMed

12.

Ajani JA, D’Amico TA, Bentrem DJ et al (2022) Gastric cancer, version 2.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 20:167–192. https://doi.org/10.6004/jnccn.2022.0008CrossRefPubMed

13.

Lordick F, Carneiro F, Cascinu S et al (2022) Gastric cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol 33:1005–1020. https://doi.org/10.1016/j.annonc.2022.07.004CrossRefPubMed

14.

Bartley AN, Mills AM, Konnick E et al (2022) Mismatch repair and microsatellite instability testing for immune checkpoint inhibitor therapy: guideline from the College of American Pathologists in Collaboration With the Association for Molecular Pathology and Fight Colorectal Cancer. Arch Pathol Lab Med 146:1194–1210. https://doi.org/10.5858/arpa.2021-0632-CPCrossRefPubMed

15.

Gullo I, Carneiro F, Oliveira C, Almeida GM (2018) Heterogeneity in gastric cancer: from pure morphology to molecular classifications. Pathobiology 85:50–63. https://doi.org/10.1159/000473881CrossRefPubMed

16.

Zhou KI, Peterson B, Serritella A et al (2020) Spatial and temporal heterogeneity of PD-L1 expression and tumor mutational burden in gastroesophageal adenocarcinoma at baseline diagnosis and after chemotherapy. Clin Cancer Res 26:6453–6463. https://doi.org/10.1158/1078-0432.CCR-20-2085CrossRefPubMedPubMedCentral

17.

Rassouli N, Etesami M, Dhanantwari A, Rajiah P (2017) Detector-based spectral CT with a novel dual-layer technology: principles and applications. Insights Imaging 8:589–598. https://doi.org/10.1007/s13244-017-0571-4CrossRefPubMedPubMedCentral

18.

Forghani R, De Man B, Gupta R (2017) Dual-energy computed tomography: physical principles, approaches to scanning, usage, and implementation: part 1. Neuroimaging Clin N Am 27:371–384. https://doi.org/10.1016/j.nic.2017.03.002CrossRefPubMed

19.

Zhang X, Zhang G, Xu L et al (2022) Utilisation of virtual non-contrast images and virtual mono-energetic images acquired from dual-layer spectral CT for renal cell carcinoma: image quality and radiation dose. Insights Imaging 13:12. https://doi.org/10.1186/s13244-021-01146-8CrossRefPubMedPubMedCentral

20.

Zhu Y, Feng B, Cai W et al (2023) Prediction of microvascular invasion in solitary AFP-negative hepatocellular carcinoma ≤ 5 cm using a combination of imaging features and quantitative dual-layer spectral-detector CT parameters. Acad Radiol S1076–6332:00088. https://doi.org/10.1016/j.acra.2023.02.015CrossRef

21.

Wang Y, Hu X, Shi S et al (2022) Utility of quantitative metrics from dual-layer spectral-detector CT for differentiation of pancreatic neuroendocrine tumor and neuroendocrine carcinoma. AJR Am J Roentgenol 218:999–1009. https://doi.org/10.2214/AJR.21.27017CrossRefPubMed

22.

Fujita N, Ushijima Y, Itoyama M et al (2023) Extracellular volume fraction determined by dual-layer spectral detector CT: possible role in predicting the efficacy of preoperative neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma. Eur J Radiol 162:110756. https://doi.org/10.1016/j.ejrad.2023.110756CrossRefPubMed

23.

Amin MB, Edge SB, Greene FL et al (2017) AJCC Cancer staging manual, 8th edn. Springer, New YorkCrossRef

24.

Wang FH, Zhang XT, Li YF et al (2021) The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer, 2021. Cancer Commun (Lond) 41:747–795. https://doi.org/10.1002/cac2.12193CrossRefPubMed

25.

Kundel HL, Polansky M (2003) Measurement of observer agreement. Radiology 228:303–308. https://doi.org/10.1148/radiol.2282011860CrossRefPubMed

26.

Birkman EM, Mansuri N, Kurki S et al (2018) Gastric cancer: immunohistochemical classification of molecular subtypes and their association with clinicopathological characteristics. Virchows Arch 472:369–382. https://doi.org/10.1007/s00428-017-2240-xCrossRefPubMed

27.

Polom K, Marano L, Marrelli D et al (2018) Meta-analysis of microsatellite instability in relation to clinicopathological characteristics and overall survival in gastric cancer. Br J Surg 105:159–167. https://doi.org/10.1002/bjs.10663CrossRefPubMed

28.

Wang J, Zhong L, Zhou X, Chen D, Li R (2021) Value of multiphase contrast-enhanced CT with three-dimensional reconstruction in detecting depth of infiltration, lymph node metastasis, and extramural vascular invasion of gastric cancer. J Gastrointest Oncol 12:1351–1362. https://doi.org/10.21037/jgo-21-276CrossRefPubMedPubMedCentral

29.

Ma Z, Liang C, Huang Y et al (2017) Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer? Eur Radiol 27:3383–3391. https://doi.org/10.1007/s00330-016-4695-6CrossRefPubMed

30.

Tsurumaru D, Miyasaka M, Muraki T et al (2017) Histopathologic diversity of gastric cancers: relationship between enhancement pattern on dynamic contrast-enhanced CT and histological type. Eur J Radiol 97:90–95. https://doi.org/10.1016/j.ejrad.2017.10.018CrossRefPubMed

31.

Tsurumaru D, Miyasaka M, Muraki T et al (2017) Diffuse-type gastric cancer: specific enhancement pattern on multiphasic contrast-enhanced computed tomography. Jpn J Radiol 35:289–295. https://doi.org/10.1007/s11604-017-0631-1CrossRefPubMed

32.

Sauter AP, Kopp FK, Münzel D et al (2018) Accuracy of iodine quantification in dual-layer spectral CT: influence of iterative reconstruction, patient habitus and tube parameters. Eur J Radiol 102:83–88. https://doi.org/10.1016/j.ejrad.2018.03.009CrossRefPubMed

33.

Mu R, Meng Z, Guo Z et al (2022) Dual-layer spectral detector computed tomography parameters can improve diagnostic efficiency of lung adenocarcinoma grading. Quant Imaging Med Surg 124:4601–4611. https://doi.org/10.21037/qims-22-2CrossRef

34.

Miyamoto N, Yamamoto H, Taniguchi H et al (2007) Differential expression of angiogenesis-related genes in human gastric cancers with and those without high-frequency microsatellite instability. Cancer Lett 254:42–53. https://doi.org/10.1016/j.canlet.2007.02.004CrossRefPubMed

35.

Lee JE, Choi YY, An JY, Kim KT, Shin SJ, Cheong JH (2022) Clinicopathologic and genomic characteristics of mucinous gastric adenocarcinoma. Gastric Cancer 25:697–711. https://doi.org/10.1007/s10120-022-01295-9CrossRefPubMed

36.

Kim JY, Shin NR, Kim A et al (2013) Microsatellite instability status in gastric cancer: a reappraisal of its clinical significance and relationship with mucin phenotypes. Korean J Pathol 47:28–35. https://doi.org/10.4132/KoreanJPathol.2013.47.1.28CrossRefPubMedPubMedCentral

37.

Zhang P, Liu M, Cui Y, Zheng P, Liu Y (2021) Microsatellite instability status differentially associates with intratumoral immune microenvironment in human cancers. Brief Bioinform 22:bbaa180. https://doi.org/10.1093/bib/bbaa180CrossRefPubMed

38.

van Velzen MJM, Derks S, van Grieken NCT, Haj Mohammad N, van Laarhoven HWM (2020) MSI as a predictive factor for treatment outcome of gastroesophageal adenocarcinoma. Cancer Treat Rev 86:102024. https://doi.org/10.1016/j.ctrv.2020.102024CrossRefPubMed

Title: Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer
Authors: Yongjian Zhu
Peng Wang
Bingzhi Wang
Zhichao Jiang
Ying Li
Jun Jiang
Yuxin Zhong
Liyan Xue
Liming Jiang
Publication date: 01-12-2023
Publisher: Springer Vienna
Keywords: Gastric Cancer
Gastric Cancer
Published in: Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-023-01490-x

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

Abstract

Objective

Methods

Results

Conclusion

Critical relevance statement

Key points

Graphical Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusion

Critical relevance statement

Key points

Graphical Abstract

Please log in to get access to this content

Other articles of this Issue 1/2023

Utilization of CT and MRI scanning in Taiwan, 2000–2017

Focal breast edema and breast edema score on T2-weighted images provides valuable biological information for invasive breast cancer

Complications of cochlear implants with MRI scans in different body regions: type, frequency and impact

Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions

High-resolution ultrasound and magnetic resonance imaging of ulnar nerve neuropathy in the distal Guyon tunnel

Does clinical decision support system promote expert consensus for appropriate imaging referrals? Chest–abdominal–pelvis CT as a case study