Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer

Abstract

Background

Advanced low rectal cancer has a non-negligible risk of lateral pelvic lymph node (LPLN) metastasis (LPLNM) and lateral local recurrence (LR) after neoadjuvant (chemo)radiotherapy and total mesorectal excision. LPLN dissection (LPLND) reduces LR but increases postoperative complications and sexual/urinary dysfunction.

Objective

The aim of this study was to develop a new radiomics-based prediction model for LPLNM in patients with rectal cancer.

Methods

A total of 247 patients with rectal cancer and enlarged LPLNs treated by (chemo)radiotherapy and LPLND were enrolled in this retrospective, multicenter study. LPLN radiomic features were extracted from pretreatment portal venous-phase computed tomography images. A radiomics score of LPLN was constructed based on the least absolute shrinkage and selection operator regression in a primary cohort of 175 patients. Model performance was assessed in terms of discrimination, calibration, and decision curve analysis, and was externally validated in 72 patients.

Results

The radiomics score showed significantly better discrimination compared with pretreatment short-axis diameter measurements in both the primary (area under the curve [AUC] 0.91 vs. 0.83, p = 0.0015) and validation (AUC 0.90 vs. 0.80, p = 0.0298) cohorts. Decision curve analysis also indicated the superiority of the radiomics score. In a subanalysis of patients with a short-axis diameter ≥ 7 mm, the radiomics nomogram, incorporating the radiomics score and LPLN shrinkage to ≤ 4 mm, had better discrimination compared with a model incorporating only LPLN shrinkage in both cohorts.

Conclusions

Radiomics-based prediction modeling provides individualized risk estimation of LPLNM in rectal cancer patients treated with (chemo)radiotherapy, and outperforms measurements of pretreatment LPLN diameter.

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References

  1. 1.

    Kapiteijn E, Marijnen CA, Nagtegaal ID, et al. Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer. N Engl J Med. 2001;345:638–46.

    CAS  PubMed  Google Scholar 

  2. 2.

    Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med. 2004;351:1731–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Bosset JF, Collette L, Calais G, et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med. 2006;355:1114–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Akiyoshi T, Watanabe T, Miyata S, Kotake K, Muto T, Sugihara K. Results of a Japanese nationwide multi-institutional study on lateral pelvic lymph node metastasis in low rectal cancer: is it regional or distant disease? Ann Surg. 2012;255:1129–34.

    PubMed  Google Scholar 

  5. 5.

    Hashiguchi Y, Muro K, Saito Y, et al. Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer. Int J Clin Oncol. 2020;25:1–42.

    PubMed  Google Scholar 

  6. 6.

    Georgiou P, Tan E, Gouvas N, et al. Extended lymphadenectomy versus conventional surgery for rectal cancer: a meta-analysis. Lancet Oncol. 2009;10:1053–62.

    PubMed  Google Scholar 

  7. 7.

    Benson AB, Venook AP, Al-Hawary MM, et al. Rectal cancer, version 2.2018, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw. 2018;16:874–901.

    Google Scholar 

  8. 8.

    Ogura A, Konishi T, Cunningham C, et al. Neoadjuvant (chemo)radiotherapy with total mesorectal excision only is not sufficient to prevent lateral local recurrence in enlarged nodes: results of the multicenter lateral node study of patients with low cT3/4 rectal cancer. J Clin Oncol. 2019;37:33–43.

    CAS  PubMed  Google Scholar 

  9. 9.

    Akiyoshi T, Ueno M, Matsueda K, et al. Selective lateral pelvic lymph node dissection in patients with advanced low rectal cancer treated with preoperative chemoradiotherapy based on pretreatment imaging. Ann Surg Oncol. 2014;21:189–96.

    PubMed  Google Scholar 

  10. 10.

    Akiyoshi T, Toda S, Tominaga T, et al. Prognostic impact of residual lateral lymph node metastasis after neoadjuvant (chemo)radiotherapy in patients with advanced low rectal cancer. BJS Open. 2019;3:822–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Kim HJ, Choi GS, Park JS, et al. Optimal treatment strategies for clinically suspicious lateral pelvic lymph node metastasis in rectal cancer. Oncotarget. 2017;8:100724–33.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Ishihara S, Kawai K, Tanaka T, et al. Oncological outcomes of lateral pelvic lymph node metastasis in rectal cancer treated with preoperative chemoradiotherapy. Dis Colon Rectum. 2017;60:469–76.

    PubMed  Google Scholar 

  13. 13.

    Malakorn S, Yang Y, Bednarski BK, et al. Who should get lateral pelvic lymph node dissection after neoadjuvant chemoradiation? Dis Colon Rectum. 2019;62:1158–66.

    PubMed  Google Scholar 

  14. 14.

    Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34:2157–64.

    PubMed  Google Scholar 

  17. 17.

    Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23:7253–62.

    CAS  PubMed  Google Scholar 

  18. 18.

    Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology. 2018;287:833–43.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Akiyoshi T, Matsueda K, Hiratsuka M, et al. Indications for lateral pelvic lymph node dissection based on magnetic resonance imaging before and after preoperative chemoradiotherapy in patients with advanced low-rectal cancer. Ann Surg Oncol. 2015;22 Suppl 3:S614–20.

    PubMed  Google Scholar 

  20. 20.

    Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30:1323–41.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–7.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–63.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16:385–95.

    CAS  PubMed  Google Scholar 

  24. 24.

    Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26:565–74.

    Google Scholar 

  25. 25.

    Ogura A, Konishi T, Beets GL, et al. Lateral nodal features on restaging magnetic resonance imaging associated with lateral local recurrence in low rectal cancer after neoadjuvant chemoradiotherapy or radiotherapy. JAMA Surg. 2019;154(9):e192172.

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Kim TG, Park W, Choi DH, et al. Factors associated with lateral pelvic recurrence after curative resection following neoadjuvant chemoradiotherapy in rectal cancer patients. Int J Colorectal Dis. 2014;29:193–200.

    PubMed  Google Scholar 

  27. 27.

    Brown G, Richards CJ, Bourne MW, et al. Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison. Radiology. 2003;227:371–7.

    PubMed  Google Scholar 

  28. 28.

    Cong M, Feng H, Ren JL, et al. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer. Lung Cancer. 2020;139:73–9.

    PubMed  Google Scholar 

  29. 29.

    Ji GW, Zhang YD, Zhang H, et al. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology. 2019;290:90–8.

    PubMed  Google Scholar 

  30. 30.

    Tan X, Ma Z, Yan L, Ye W, Liu Z, Liang C. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol. 2019;29:392–400.

    PubMed  Google Scholar 

  31. 31.

    Zhu H, Zhang X, Li X, Shi Y, Zhu H, Sun Y. Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy. Chin J Cancer Res. 2019;31:984–92.

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Cercek A, Roxburgh CSD, Strombom P, et al. Adoption of total neoadjuvant therapy for locally advanced rectal cancer. JAMA Oncol. 2018;4:e180071.

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Konishi T, Shinozaki E, Murofushi K, et al. Phase II trial of neoadjuvant chemotherapy, chemoradiotherapy, and laparoscopic surgery with selective lateral node dissection for poor-risk low rectal cancer. Ann Surg Oncol. 2019;26:2507–13.

    PubMed  Google Scholar 

  34. 34.

    Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010;11:835–44.

    PubMed  Google Scholar 

  35. 35.

    van Timmeren JE, Leijenaar RTH, van Elmpt W, et al. Test-retest data for radiomics feature stability analysis: generalizable or study-specific? Tomography. 2016;2:361–5.

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Varghese BA, Hwang D, Cen SY, et al. Reliability of CT-based texture features: phantom study. J Appl Clin Med Phys. 2019;20:155–63.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Bagher-Ebadian H, Siddiqui F, Liu C, Movsas B, Chetty IJ. On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers. Med Phys. 2017;44:1755–70.

    PubMed  Google Scholar 

  38. 38.

    Yang F, Dogan N, Stoyanova R, Ford JC. Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: a simulation study utilizing ground truth. Phys Med. 2018;50:26–36.

    PubMed  Google Scholar 

  39. 39.

    Fiset S, Welch ML, Weiss J, et al. Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. Radiother Oncol. 2019;135:107–14.

    PubMed  Google Scholar 

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Funding

This study was supported in part by a Japanese Foundation for Research and Promotion of Endoscopy Grant, and JSPS KAKENHI Grant Number 20K09022.

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Authors

Contributions

Conception and design: Takashi Akiyoshi. Acquisition of data: All authors. Analysis and interpretation of data: Ryota Nakanishi, Takashi Akiyoshi, Shigeo Toda, Senzo Taguchi, Yu Murakami, and Koji Oba. Manuscript writing: All authors. Final approval of the manuscript: All authors.

Corresponding author

Correspondence to Takashi Akiyoshi MD, PhD.

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Disclosures

Ryota Nakanishi, Takashi Akiyoshi, Shigeo Toda, Yu Murakami, Senzo Taguchi, Koji Oba, Yutaka Hanaoka, Toshiya Nagasaki, Tomohiro Yamaguchi, Tsuyoshi Konishi, Shuichiro Matoba, Masashi Ueno, Yosuke Fukunaga, and Hiroya Kuroyanagi declare they have no conflicts of interest.

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Nakanishi, R., Akiyoshi, T., Toda, S. et al. Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer. Ann Surg Oncol 27, 4273–4283 (2020). https://doi.org/10.1245/s10434-020-08974-w

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