Skip to content
2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Objective

This study aims to develop an ultrasomics model for predicting lymph node metastasis in patients with gastric cancer (GC).

Methods

This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs).

Results

A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95% CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95% CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status.

Conclusion

Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056291074240522052725
2024-01-01
2025-07-10
The full text of this item is not currently available.

References

  1. SungH. FerlayJ. SiegelR.L. LaversanneM. SoerjomataramI. JemalA. BrayF. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.2166033538338
    [Google Scholar]
  2. SmythE.C. NilssonM. GrabschH.I. van GriekenN.C.T. LordickF. Gastric cancer.Lancet20203961025163564810.1016/S0140‑6736(20)31288‑532861308
    [Google Scholar]
  3. LiK. ZhangA. LiX. ZhangH. ZhaoL. Advances in clinical immunotherapy for gastric cancer.Biochim. Biophys. Acta Rev. Cancer20211876218861510.1016/j.bbcan.2021.18861534403771
    [Google Scholar]
  4. KimJ.P. HurY.S. YangH.K. Lymph node metastasis as a significant prognostic factor in early gastric cancer: Analysis of 1,136 early gastric cancers.Ann. Surg. Oncol.19952430831310.1007/BF023070627552619
    [Google Scholar]
  5. OnoH. YaoK. FujishiroM. OdaI. NimuraS. YahagiN. IishiH. OkaM. AjiokaY. IchinoseM. MatsuiT. Guidelines for endoscopic submucosal dissection and endoscopic mucosal resection for early gastric cancer.Dig. Endosc.201628131510.1111/den.1251826234303
    [Google Scholar]
  6. AjaniJ.A. BentremD.J. BeshS. D’AmicoT.A. DasP. DenlingerC. FakihM.G. FuchsC.S. GerdesH. GlasgowR.E. HaymanJ.A. HofstetterW.L. IlsonD.H. KeswaniR.N. KleinbergL.R. KornW.M. LockhartA.C. MeredithK. MulcahyM.F. OrringerM.B. PoseyJ.A. SassonA.R. ScottW.J. StrongV.E. VargheseT.K.Jr WarrenG. WashingtonM.K. WillettC. WrightC.D. McMillianN.R. SundarH. National Comprehensive Cancer Network Gastric cancer, version 2.2013: Featured updates to the NCCN Guidelines.J. Natl. Compr. Canc. Netw.201311553154610.6004/jnccn.2013.007023667204
    [Google Scholar]
  7. OkaS. TanakaS. KanekoI. MouriR. HirataM. KawamuraT. YoshiharaM. ChayamaK. Advantage of endoscopic submucosal dissection compared with EMR for early gastric cancer.Gastrointest. Endosc.200664687788310.1016/j.gie.2006.03.93217140890
    [Google Scholar]
  8. KimH.J. KimA.Y. OhS.T. KimJ.S. KimK.W. KimP.N. LeeM.G. HaH.K. Gastric cancer staging at multi-detector row CT gastrography: Comparison of transverse and volumetric CT scanning.Radiology2005236387988510.1148/radiol.236304110116020558
    [Google Scholar]
  9. DongD. FangM.J. TangL. ShanX.H. GaoJ.B. GigantiF. WangR.P. ChenX. WangX.X. PalumboD. FuJ. LiW.C. LiJ. ZhongL.Z. De CobelliF. JiJ.F. LiuZ.Y. TianJ. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: An international multicenter study.Ann. Oncol.202031791292010.1016/j.annonc.2020.04.00332304748
    [Google Scholar]
  10. LiuZ. RenW. GuoJ. ZhaoY. SunS. LiY. LiuZ. Preliminary opinion on assessment categories of stomach ultrasound report and data system (Su-RADS).Gastric Cancer201821587988810.1007/s10120‑018‑0798‑x29372460
    [Google Scholar]
  11. ShenL. ZhouC. LiuL. ZhangL. LuD. CaiJ. ZhaoL. ChuR. ZhouJ. ZhangJ. Application of oral contrast trans-abdominal ultrasonography for initial screening of gastric cancer in rural areas of China.Dig. Liver Dis.201749891892310.1016/j.dld.2017.04.00828487084
    [Google Scholar]
  12. LiuZ. GuoJ. LiJ. WangS. TangS. XieL. HuangY. LuW. RenW. SunS. HuangL. Gastric lesions: Demonstrated by transabdominal ultrasound after oral administration of an echoic cellulose-based gastric ultrasound contrast agent.Ultraschall Med.201637440541126114343
    [Google Scholar]
  13. LambinP. LeijenaarR.T.H. DeistT.M. PeerlingsJ. de JongE.E.C. van TimmerenJ. SanduleanuS. LarueR.T.H.M. EvenA.J.G. JochemsA. van WijkY. WoodruffH. van SoestJ. LustbergT. RoelofsE. van ElmptW. DekkerA. MottaghyF.M. WildbergerJ.E. WalshS. Radiomics: The bridge between medical imaging and personalized medicine.Nat. Rev. Clin. Oncol.2017141274976210.1038/nrclinonc.2017.14128975929
    [Google Scholar]
  14. GilliesR.J. KinahanP.E. HricakH. Radiomics: Images are more than pictures, they are data.Radiology2016278256357710.1148/radiol.201515116926579733
    [Google Scholar]
  15. LinP. LinY. GaoR. WanW. HeY. YangH. Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer.Eur. Radiol.20233396414642510.1007/s00330‑023‑09503‑536826501
    [Google Scholar]
  16. NiyotekaS. SebanR.D. RouhiR. ScarsbrookA. GenestieC. ClasseM. CarréA. SunR. La Greca Saint-EstevenA. ChargariC. McKennaJ. McDermottG. MalinenE. Tanadini-LangS. GuckenbergerM. GurenM.G. LemanskiC. DeutschE. RobertC. A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers.Eur. J. Nucl. Med. Mol. Imaging202350134010402310.1007/s00259‑023‑06320‑237632562
    [Google Scholar]
  17. SunR. LimkinE.J. VakalopoulouM. DercleL. ChampiatS. HanS.R. VerlingueL. BrandaoD. LanciaA. AmmariS. HollebecqueA. ScoazecJ.Y. MarabelleA. MassardC. SoriaJ.C. RobertC. ParagiosN. DeutschE. FertéC. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: An imaging biomarker, retrospective multicohort study.Lancet Oncol.20181991180119110.1016/S1470‑2045(18)30413‑330120041
    [Google Scholar]
  18. WangY. LiuW. YuY. LiuJ. XueH. QiY. LeiJ. YuJ. JinZ. CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer.Eur. Radiol.202030297698610.1007/s00330‑019‑06398‑z31468157
    [Google Scholar]
  19. LiJ. DongD. FangM. WangR. TianJ. LiH. GaoJ. Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.Eur. Radiol.20203042324233310.1007/s00330‑019‑06621‑x31953668
    [Google Scholar]
  20. GaoX. MaT. CuiJ. ZhangY. WangL. LiH. YeZ. A CT-based radiomics model for prediction of lymph node metastasis in early stage gastric cancer.Acad. Radiol.2021286e155e16410.1016/j.acra.2020.03.04532507613
    [Google Scholar]
  21. LiuQ. LiJ. XinB. SunY. FengD. FulhamM.J. WangX. SongS. 18F-FDG PET/CT radiomics for preoperative prediction of lymph node metastases and nodal staging in gastric cancer.Front. Oncol.20211172334510.3389/fonc.2021.72334534589429
    [Google Scholar]
  22. YinR. JiangM. LvW.Z. JiangF. LiJ. HuB. CuiX.W. DietrichC.F. Study processes and applications of ultrasomics in precision medicine.Front. Oncol.202010173610.3389/fonc.2020.0173633014858
    [Google Scholar]
  23. YushkevichP.A. PivenJ. HazlettH.C. SmithR.G. HoS. GeeJ.C. GerigG. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability.Neuroimage20063131116112810.1016/j.neuroimage.2006.01.01516545965
    [Google Scholar]
  24. van GriethuysenJ.J.M. FedorovA. ParmarC. HosnyA. AucoinN. NarayanV. Beets-TanR.G.H. Fillion-RobinJ.C. PieperS. AertsH.J.W.L. Computational radiomics system to decode the radiographic phenotype.Cancer Res.20177721e104e10710.1158/0008‑5472.CAN‑17‑033929092951
    [Google Scholar]
  25. YangJ. WuQ. XuL. WangZ. SuK. LiuR. YenE.A. LiuS. QinJ. RongY. LuY. NiuT. Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer.Radiother. Oncol.2020150899610.1016/j.radonc.2020.06.00432531334
    [Google Scholar]
  26. KinamiS. SaitoH. TakamuraH. Significance of lymph node metastasis in the treatment of gastric cancer and current challenges in determining the extent of metastasis.Front. Oncol.20221180616210.3389/fonc.2021.80616235071010
    [Google Scholar]
  27. MayerhoeferM.E. MaterkaA. LangsG. HäggströmI. SzczypińskiP. GibbsP. CookG. Introduction to radiomics.J. Nucl. Med.202061448849510.2967/jnumed.118.22289332060219
    [Google Scholar]
  28. CuiY. ZhangJ. LiZ. WeiK. LeiY. RenJ. WuL. ShiZ. MengX. YangX. GaoX. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.EClinicalMedicine20224610134810.1016/j.eclinm.2022.10134835340629
    [Google Scholar]
  29. YangL. ChuW. LiM. XuP. WangM. PengM. WangK. ZhangL. Radiomics in gastric cancer: First Clinical Investigation to Predict lymph vascular invasion and survival outcome using 18F-FDG PET/CT images.Front. Oncol.20221283609810.3389/fonc.2022.83609835433451
    [Google Scholar]
  30. HaoD. LiQ. FengQ.X. QiL. LiuX.S. ArefanD. ZhangY.D. WuS. Identifying prognostic markers from clinical, radiomics, and deep learning imaging features for gastric cancer survival prediction.Front. Oncol.20221172588910.3389/fonc.2021.72588935186707
    [Google Scholar]
  31. WeiY. LuZ. RenY. Predictive value of a radiomics nomogram model based on contrast-enhanced computed tomography for KIT Exon 9 gene mutation in gastrointestinal stromal tumors.Technol. Cancer Res. Treat.20232210.1177/1533033823118126037296525
    [Google Scholar]
  32. HuY. LiA. ZhaoC.K. YeX.H. PengX.J. WangP.P. ShuH. YaoQ.Y. LiuW. LiuY.Y. LvW.Z. XuH.X. A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: A combined retrospective and prospective bicentric study.Radiol. Med.2023128678479710.1007/s11547‑023‑01639‑037154999
    [Google Scholar]
  33. ZhangL. DuanS. QiQ. LiQ. RenS. LiuS. MaoB. ZhangY. WangS. YangL. LiuR. LiuL. LiY. LiN. ZhangL. Noninvasive prediction of Ki‐67 expression in hepatocellular carcinoma using machine learning‐based ultrasomics.J. Ultrasound Med.20234251113112210.1002/jum.1612636412932
    [Google Scholar]
  34. XuR. YouT. LiuC. LinQ. GuoQ. ZhongG. LiuL. OuyangQ. Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer.Front. Oncol.202313121644610.3389/fonc.2023.121644637583930
    [Google Scholar]
  35. RenY. LuS. ZhangD. WangX. AgyekumE.A. ZhangJ. ZhangQ. XuF. ZhangG. ChenY. ShenX. ZhangX. WuT. HuH. ShanX. WangJ. QianX. Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma.J. XRay Sci. Technol.20233161263128010.3233/XST‑23009137599557
    [Google Scholar]
  36. ParkA.Y. HanM.R. SeoB.K. JuH.Y. SonG.S. LeeH.Y. ChangY.W. ChoiJ. ChoK.R. SongS.E. WooO.H. ParkH.S. MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study.Breast Cancer Res.20232517910.1186/s13058‑023‑01668‑737391754
    [Google Scholar]
  37. DongW. XiongS. WangX. HuS. LiuY. LiuH. WangX. ChenJ. QiuY. FanB. Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.J. Cancer Res. Clin. Oncol.202314916149011491010.1007/s00432‑023‑05263‑337604939
    [Google Scholar]
  38. MahmoudianM. VenäläinenM.S. KlénR. EloL.L. Stable iterative variable selection.Bioinformatics202137244810481710.1093/bioinformatics/btab50134270690
    [Google Scholar]
/content/journals/cmir/10.2174/0115734056291074240522052725
Loading
/content/journals/cmir/10.2174/0115734056291074240522052725
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test