Skip to main content
SpringerLink
Log in

Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements

  • Hollow Organ GI
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs).

Methods

Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared.

Results

The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 – p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618).

Conclusions

Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Tirumani SH, Baheti AD, Tirumani H, O'Neill A, Jagannathan JP (2017) Update on Gastrointestinal Stromal Tumors for Radiologists. Korean J Radiol 18 (1):84-93. doi:https://doi.org/10.3348/kjr.2017.18.1.84

    Article  PubMed  PubMed Central  Google Scholar 

  2. Parab TM, DeRogatis MJ, Boaz AM, Grasso SA, Issack PS, Duarte DA, Urayeneza O, Vahdat S, Qiao JH, Hinika GS (2019) Gastrointestinal stromal tumors: a comprehensive review. Journal of gastrointestinal oncology 10 (1):144-154. doi:https://doi.org/10.21037/jgo.2018.08.20

    Article  PubMed  PubMed Central  Google Scholar 

  3. Sepe PS, Brugge WR (2009) A guide for the diagnosis and management of gastrointestinal stromal cell tumors. Nat Rev Gastroenterol Hepatol 6 (6):363-371. doi:https://doi.org/10.1038/nrgastro.2009.43

    Article  PubMed  Google Scholar 

  4. Joensuu H (2008) Risk stratification of patients diagnosed with gastrointestinal stromal tumor. Human Pathology 39 (10):1411-1419. doi:https://doi.org/10.1016/j.humpath.2008.06.025

    Article  PubMed  Google Scholar 

  5. Iannicelli E, Carbonetti F, Federici GF, Martini I, Caterino S, Pilozzi E, Panzuto F, Briani C, David V (2017) Evaluation of the Relationships Between Computed Tomography Features, Pathological Findings, and Prognostic Risk Assessment in Gastrointestinal Stromal Tumors. Journal of Computer Assisted Tomography 41 (2):271-278. doi:https://doi.org/10.1097/rct.0000000000000499

    Article  PubMed  Google Scholar 

  6. Zhang X, Bai L, Wang D, Huang X, Wei J, Zhang W, Zhang Z, Zhou J (2019) Gastrointestinal stromal tumor risk classification: spectral CT quantitative parameters. Abdom Radiol (NY) 44 (7):2329-2336. doi:https://doi.org/10.1007/s00261-019-01973-w

    Article  PubMed  Google Scholar 

  7. Meyer M, Hohenberger P, Overhoff D, Bartsch A, Henzler T, Haubenreisser H, Ronald J, Schmidt B, Flohr T, Sedlmair M, Ota H, Messiou C, Schoenberg SO, Riedel RF, Nelson RC, Marin D (2022) Dual-Energy CT Vital Iodine Tumor Burden for Response Assessment in Patients With Metastatic GIST Undergoing TKI Therapy: Comparison With Standard CT and FDG PET/CT Criteria. AJR American journal of roentgenology 218 (4):659-669. doi:https://doi.org/10.2214/ajr.21.26636

    Article  PubMed  Google Scholar 

  8. Ozturk M, Polat AV, Selcuk MB (2021) Whole-lesion ADC histogram analysis versus single-slice ADC measurement for the differentiation of benign and malignant soft tissue tumors. Eur J Radiol 143:109934. doi:https://doi.org/10.1016/j.ejrad.2021.109934

    Article  PubMed  Google Scholar 

  9. Ma G, Zhu LN, Su GY, Hu H, Qian W, Bu SS, Xu XQ, Wu FY (2018) Histogram analysis of apparent diffusion coefficient maps for differentiating malignant from benign parotid gland tumors. Eur Arch Otorhinolaryngol 275 (8):2151-2157. doi:https://doi.org/10.1007/s00405-018-5052-y

    Article  PubMed  Google Scholar 

  10. Gihr G, Horvath-Rizea D, Kohlhof-Meinecke P, Ganslandt O, Henkes H, Hartig W, Donitza A, Skalej M, Schob S (2022) Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization. Cancers (Basel) 14 (14). doi:https://doi.org/10.3390/cancers14143393

  11. Borchers A, Pieler T (2010) Programming pluripotent precursor cells derived from Xenopus embryos to generate specific tissues and organs. Genes (Basel) 1 (3):413-426. doi:https://doi.org/10.3390/genes1030413

    Article  CAS  PubMed  Google Scholar 

  12. Kunimatsu N, Kunimatsu A, Miura K, Mori I, Nawano S (2019) Differentiation between solitary fibrous tumors and schwannomas of the head and neck: an apparent diffusion coefficient histogram analysis. Dentomaxillofac Radiol 48 (3):20180298. doi:https://doi.org/10.1259/dmfr.20180298

    Article  PubMed  PubMed Central  Google Scholar 

  13. Minh Duc N (2022) The impact of ADC-histogram parameters on the discrimina-tion between medulloblastoma, ependymoma, and pilocytic astrocytoma. Clin Ter 173 (4):369-376. doi:https://doi.org/10.7417/CT.2022.2448

    Article  CAS  PubMed  Google Scholar 

  14. Qi M, Xia Z, Zhang F, Sha Y, Ren J (2023) Development and validation of apparent diffusion coefficient histogram-based nomogram for predicting malignant transformation of sinonasal inverted papilloma. Dentomaxillofac Radiol:20220301. doi:https://doi.org/10.1259/dmfr.20220301

  15. Romano A, Pasquini L, Di Napoli A, Tavanti F, Boellis A, Rossi Espagnet MC, Minniti G, Bozzao A (2018) Prediction of survival in patients affected by glioblastoma: histogram analysis of perfusion MRI. J Neurooncol 139 (2):455-460. doi:https://doi.org/10.1007/s11060-018-2887-4

    Article  CAS  PubMed  Google Scholar 

  16. Wang W, Cheng J, Zhang Y, Wang C (2018) Use of Apparent Diffusion Coefficient Histogram in Differentiating Between Medulloblastoma and Pilocytic Astrocytoma in Children. Med Sci Monit 24:6107-6112. doi:https://doi.org/10.12659/MSM.909136

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lenga L, Bernatz S (2010) Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status. Genes (Basel) 1 (3):413-426. doi:https://doi.org/10.3390/genes1030413

    Article  CAS  Google Scholar 

  18. Zeng F, Chen L, Lin L, Hu H, Li J, He P, Wang C, Xue Y (2022) Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status. Quant Imaging Med Surg 12 (12):5358-5370. doi:https://doi.org/10.21037/qims-22-253

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lu J, Hu D, Tang H, Hu X, Shen Y, Li Z, Peng Y, Kamel I (2019) Assessment of tumor heterogeneity: Differentiation of periampullary neoplasms based on CT whole-lesion histogram analysis. European Journal of Radiology 115:1-9. doi:https://doi.org/10.1016/j.ejrad.2019.03.021

    Article  PubMed  Google Scholar 

  20. Nougaret S, Vargas HA, Lakhman Y, Sudre R, Do RK, Bibeau F, Azria D, Assenat E, Molinari N, Pierredon MA, Rouanet P, Guiu B (2016) Intravoxel Incoherent Motion-derived Histogram Metrics for Assessment of Response after Combined Chemotherapy and Radiation Therapy in Rectal Cancer: Initial Experience and Comparison between Single-Section and Volumetric Analyses. Radiology 280 (2):446-454. doi:https://doi.org/10.1148/radiol.2016150702

    Article  PubMed  Google Scholar 

Download references

Funding

This study was supported by grants from the National Natural Science Foundation of China [Grant Number: 82071872]; and the Youth Science and Technology Talent Innovation Project of Lanzhou [Grant Number: 2023-2-44].

Author information

Author notes

  1. Yijing Xie and Shipeng Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The Second Hospital of Lanzhou University, Lanzhou, 730000, China

    Yijing Xie, Xianwang Liu & Junlin Zhou

  2. Department of Nuclear Medicine, The Second Hospital of Lanzhou University, Lanzhou, 730000, China

    Yongjun Luo

  3. The Second Clinical Medical School, Lanzhou University, Lanzhou, 730000, China

    Yijing Xie, Xianwang Liu & Yongjun Luo

  4. Department of Radiology, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, 730000, China

    Shipeng Zhang

  5. Key Laboratory of Medical Imaging of Gansu Province, The Second Hospital of Lanzhou University, Lanzhou, 730000, China

    Yijing Xie, Xianwang Liu, Yongjun Luo & Junlin Zhou

  6. Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730000, China

    Yijing Xie, Xianwang Liu, Yongjun Luo & Junlin Zhou

Authors
  1. Yijing Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shipeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianwang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongjun Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junlin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YX: conceptualization, methodology, data curation, writing—original draft. SZ: methodology, writing—original draft. XL: statistical analysis, resources, revision. YL: visualization. JZ: conceptualization, methodology, supervision, funding acquisition.

Corresponding author

Correspondence to Junlin Zhou.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

The study was approved by the Institutional Review Board, and informed consent was waived due to retrospective analysis of the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, Y., Zhang, S., Liu, X. et al. Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04224-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00261-024-04224-9

Keywords

Search

Navigation