Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Introduction

The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) images.

Methods

Fifty-nine tumors with stage 2 or 3 rectal cancer that received nCRT were retrospectively evaluated. Pathological tumor regression grading was carried out using the Dworak (Dw-TRG) guidelines and served as the ground truth for response predictions. Imaging-based tumor regression grading was performed according to the MERCURY group guidelines from pre-treatment and post-treatment para-axial T2-weighted MR images (MR-TRG). Tumor signal intensity signatures were extracted by segmenting the tumors volumetrically on the images. Normalized histograms of the signatures were used as input to a deep neural network (DNN) housing long short-term memory (LSTM) units. The output of the network was the tumor regression grading prediction, DNN-TRG.

Results

In predicting complete or good response, DNN-TRG demonstrated modest agreement with Dw-TRG (Cohen’s kappa= 0.79) and achieved 84.6% sensitivity, 93.9% specificity, and 89.8% accuracy. MR-TRG revealed 46.2% sensitivity, 100% specificity, and 76.3% accuracy. In predicting a complete response, DNN-TRG showed slight agreement with Dw-TRG (Cohen’s kappa= 0.75) with 71.4% sensitivity, 97.8% specificity, and 91.5% accuracy. MR-TRG provided 42.9% sensitivity, 100% specificity, and 86.4% accuracy. DNN-TRG benefited from higher sensitivity but lower specificity, leading to higher accuracy than MR-TRG in predicting tumor response.

Conclusion

The use of deep LSTM neural networks is a promising approach for evaluating the tumor response to nCRT in rectal cancer.

© 2024 The Author(s). Published by Bentham Science Publisher.
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/10.2174/0115734056309748240509072222
2024-01-01
2025-06-22

  • From This Site

    /content/journals/cmir/10.2174/0115734056309748240509072222
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
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. HorvatN. VeeraraghavanH. KhanM. BlazicI. ZhengJ. CapanuM. SalaE. Garcia-AguilarJ. GollubM.J. PetkovskaI. MR imaging of rectal cancer: Radiomics analysis to assess treatment response after neoadjuvant therapy.Radiology2018287383384310.1148/radiol.201817230029514017
     [Google Scholar]
  3. OronskyB. ReidT. LarsonC. KnoxS.J. Locally advanced rectal cancer: The past, present, and future.Semin. Oncol.2020471859210.1053/j.seminoncol.2020.02.00132147127
     [Google Scholar]
  4. GlimeliusB. TiretE. CervantesA. ArnoldD. Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up.Ann. Oncol.201324Suppl. 6vi81vi8810.1093/annonc/mdt24024078665
     [Google Scholar]
  5. PakH. MaghsoudiL.H. SoltanianA. GholamiF. Surgical complications in colorectal cancer patients.Ann. Med. Surg. (Lond.)202055131810.1016/j.amsu.2020.04.02432435475
     [Google Scholar]
  6. DixonL.K. BiggsS. MessengerD. ShabbirJ. Surgical site infection prevention bundle in elective colorectal surgery.J. Hosp. Infect.202212216216710.1016/j.jhin.2022.01.02335151765
     [Google Scholar]
  7. FalconerR. RamsayG. HudsonJ. WatsonA. Reducing surgical site infection rates in colorectal surgery – a quality improvement approach to implementing a comprehensive bundle.Colorectal Dis.202123112999300710.1111/codi.1587534396654
     [Google Scholar]
  8. TurnerM. MigalyJ. Surgical site infection: The clinical and economic impact.Clin. Colon Rectal Surg.201932315716510.1055/s‑0038‑167700231061644
     [Google Scholar]
  9. ZhangX. WangZ. ChenJ. WangP. LuoS. XuX. MaiW. LiG. WangG. WuX. RenJ. Incidence and risk factors of surgical site infection following colorectal surgery in China: A national cross-sectional study.BMC Infect. Dis.202020183710.1186/s12879‑020‑05567‑633183253
     [Google Scholar]
  10. MaasM. Beets-TanR.G.H. LambregtsD.M.J. LammeringG. NelemansP.J. EngelenS.M.E. van DamR.M. JansenR.L.H. SosefM. LeijtensJ.W.A. HulsewéK.W.E. BuijsenJ. BeetsG.L. Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer.J. Clin. Oncol.201129354633464010.1200/JCO.2011.37.717622067400
     [Google Scholar]
  11. O’NeillB.D.P. BrownG. HealdR.J. CunninghamD. TaitD.M. Non-operative treatment after neoadjuvant chemoradiotherapy for rectal cancer.Lancet Oncol.20078762563310.1016/S1470‑2045(07)70202‑417613424
     [Google Scholar]
  12. HarewoodG.C. KumarK.S. ClainJ. LevyM.J. NelsonH. Clinical implications of quantification of mesorectal tumor invasion by endoscopic ultrasound: All T3 rectal cancers are not equal.J. Gastroenterol. Hepatol.200419775075510.1111/j.1440‑1746.2004.03356.x15209620
     [Google Scholar]
  13. EsclapezP. Garcia-GraneroE. FlorB. García-BotelloS. CervantesA. NavarroS. LledóS. Prognostic heterogeneity of endosonographic T3 rectal cancer.Dis. Colon Rectum200952468569110.1007/DCR.0b013e31819ed03d19404075
     [Google Scholar]
  14. ZhuH.T. ZhangX.Y. ShiY.J. LiX.T. SunY.S. A deep learning model to predict the response to neoadjuvant chemoradiotherapy by the pretreatment apparent diffusion coefficient images of locally advanced rectal cancer.Front. Oncol.20201057433710.3389/fonc.2020.57433733194680
     [Google Scholar]
  15. GollinsS. WestN. Sebag-MontefioreD. SusnerwalaS. FalkS. BrownN. SaundersM. QuirkeP. RayR. ParsonsP. GriffithsG. MaughanT. AdamsR. HurtC. A prospective phase II study of pre-operative chemotherapy then short-course radiotherapy for high risk rectal cancer: COPERNICUS.Br. J. Cancer2018119669770610.1038/s41416‑018‑0209‑430116024
     [Google Scholar]
  16. PatelU.B. BrownG. RuttenH. WestN. Sebag-MontefioreD. Glynne-JonesR. RullierE. PeetersM. Van CutsemE. RicciS. Van de VeldeC. KjellP. QuirkeP. Comparison of magnetic resonance imaging and histopathological response to chemoradiotherapy in locally advanced rectal cancer.Ann. Surg. Oncol.20121992842285210.1245/s10434‑012‑2309‑322526897
     [Google Scholar]
  17. Al-SukhniE. MilotL. FruitmanM. BeyeneJ. VictorJ.C. SchmockerS. BrownG. McLeodR. KennedyE. Diagnostic accuracy of MRI for assessment of T category, lymph node metastases, and circumferential resection margin involvement in patients with rectal cancer: A systematic review and meta-analysis.Ann. Surg. Oncol.20121972212222310.1245/s10434‑011‑2210‑522271205
     [Google Scholar]
  18. MemonS. LynchA.C. BresselM. WiseA.G. HeriotA.G. Systematic review and meta‐analysis of the accuracy of MRI and endorectal ultrasound in the restaging and response assessment of rectal cancer following neoadjuvant therapy.Colorectal Dis.201517974876110.1111/codi.1297625891148
     [Google Scholar]
  19. HötkerA.M. TarlintonL. MazaheriY. WooK.M. GönenM. SaltzL.B. GoodmanK.A. Garcia-AguilarJ. GollubM.J. Multiparametric MRI in the assessment of response of rectal cancer to neoadjuvant chemoradiotherapy: A comparison of morphological, volumetric and functional MRI parameters.Eur. Radiol.201626124303431210.1007/s00330‑016‑4283‑926945761
     [Google Scholar]
  20. BrownG. RadcliffeA.G. NewcombeR.G. DallimoreN.S. BourneM.W. WilliamsG.T. Preoperative assessment of prognostic factors in rectal cancer using high-resolution magnetic resonance imaging.Br. J. Surg.200390335536410.1002/bjs.403412594673
     [Google Scholar]
  21. QureshiP.A.A.A. AleemJ. MushtaqN. NoorM.A. Khalid NiaziI. AltafM.O. Role of Diffusion-weighted imaging in the evaluation of post-treatment tumor response in rectal carcinoma.Cureus2021138e1747110.7759/cureus.1747134589364
     [Google Scholar]
  22. KartalM.G. AzamatS. KaramanŞ. AzamatI.F. ErtaşG. KulleC.B. KeskinM. SakinR.N.D. BakırB. OralE.N. Complete response evaluation of locally advanced rectal cancer to neoadjuvant chemoradiotherapy using textural features obtained from T2 weighted imaging and ADC maps.Curr. Med. Imaging Rev.202218101061106910.2174/157340561866622030311102635240976
     [Google Scholar]
  23. De CeccoC.N. GaneshanB. CiolinaM. RengoM. MeinelF.G. MusioD. De FeliceF. RaffettoN. TomboliniV. LaghiA. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance.Invest. Radiol.201550423924510.1097/RLI.000000000000011625501017
     [Google Scholar]
  24. EstevaA. KuprelB. NovoaR.A. KoJ. SwetterS.M. BlauH.M. ThrunS. Dermatologist-level classification of skin cancer with deep neural networks.Nature2017542763911511810.1038/nature2105628117445
     [Google Scholar]
  25. MiottoR. LiL. KiddB.A. DudleyJ.T. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records.Sci. Rep.2016612609410.1038/srep2609427185194
     [Google Scholar]
  26. ChlorogiannisD.D. VerrasG.I. TzelepiV. ChlorogiannisA. ApostolosA. KotisK. AnagnostopoulosC.N. AntzoulasA. VailasM. SchizasD. MulitaF. MulitaF. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?Prz. Gastroenterol.202318435336710.5114/pg.2023.13033738572457
     [Google Scholar]
  27. DavriA. BirbasE. KanavosT. NtritsosG. GiannakeasN. TzallasA.T. BatistatouA. Deep learning on histopathological images for colorectal cancer diagnosis: A systematic review.Diagnostics (Basel)202212483710.3390/diagnostics1204083735453885
     [Google Scholar]
  28. BibaultJ.E. GiraudP. HoussetM. DurduxC. TaiebJ. BergerA. CoriatR. ChaussadeS. DoussetB. NordlingerB. BurgunA. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer.Sci. Rep.2018811261110.1038/s41598‑018‑30657‑630135549
     [Google Scholar]
  29. JangB.S. LimY.J. SongC. JeonS.H. LeeK.W. KangS.B. LeeY.J. KimJ.S. Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging.Radiother. Oncol.202116118319010.1016/j.radonc.2021.06.01934139211
     [Google Scholar]
  30. ShiL. ZhangY. NieK. SunX. NiuT. YueN. KwongT. ChangP. ChowD. ChenJ.H. SuM.Y. Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.Magn. Reson. Imaging201961334010.1016/j.mri.2019.05.00331059768
     [Google Scholar]
  31. ZhangX.Y. WangL. ZhuH.T. LiZ.W. YeM. LiX.T. ShiY.J. ZhuH.C. SunY.S. Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI.Radiology20202961566410.1148/radiol.202019093632315264
     [Google Scholar]
  32. Habr-GamaA. PerezR.O. WynnG. MarksJ. KesslerH. Gama-RodriguesJ. Complete clinical response after neoadjuvant chemoradiation therapy for distal rectal cancer: Characterization of clinical and endoscopic findings for standardization.Dis. Colon Rectum201053121692169810.1007/DCR.0b013e3181f42b8921178866
     [Google Scholar]
  33. DworakO. KeilholzL. HoffmannA. Pathological features of rectal cancer after preoperative radiochemotherapy.Int. J. Colorectal Dis.1997121192310.1007/s0038400500729112145
     [Google Scholar]
  34. BattersbyN.J. HowP. MoranB. StelznerS. WestN.P. BranaganG. StrassburgJ. QuirkeP. TekkisP. PedersenB.G. GudgeonM. HealdB. BrownG. Prospective validation of a low rectal cancer magnetic resonance imaging staging system and development of a local recurrence risk stratification model.Ann. Surg.2016263475176010.1097/SLA.000000000000119325822672
     [Google Scholar]
  35. BrownG. DaviesS. WilliamsG.T. BourneM.W. NewcombeR.G. RadcliffeA.G. BlethynJ. DallimoreN.S. ReesB.I. PhillipsC.J. MaughanT.S. Effectiveness of preoperative staging in rectal cancer: Digital rectal examination, endoluminal ultrasound or magnetic resonance imaging?Br. J. Cancer2004911232910.1038/sj.bjc.660187115188013
     [Google Scholar]
  36. Curvo-SemedoL. LambregtsD.M.J. MaasM. ThywissenT. MehsenR.T. LammeringG. BeetsG.L. Caseiro-AlvesF. Beets-TanR.G.H. Rectal cancer: Assessment of complete response to preoperative combined radiation therapy with chemotherapy--conventional MR volumetry versus diffusion-weighted MR imaging.Radiology2011260373474310.1148/radiol.1110246721673229
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056309748240509072222
Loading
Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks
Curr. Med. Imaging 20, e15734056309748 (2024); https://doi.org/10.2174/0115734056309748240509072222
/content/journals/cmir/10.2174/0115734056309748240509072222
/content/journals/cmir/10.2174/0115734056309748240509072222
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Deep learning; Long short-term memory; Magnetic resonance imaging; MR-TRG; Neoadjuvant chemoradiotherapy; Rectal cancer

Most Cited Most Cited RSS feed

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