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Abdominal Radiology

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14.08.2023 | Hollow Organ GI

Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer

verfasst von: Feiwen Feng, Yuanqing Liu, Jiayi Bao, Rong Hong, Su Hu, Chunhong Hu

Erschienen in: Abdominal Radiology | Ausgabe 11/2023

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Abstract

Purpose

To establish and validate an integrated model incorporating multiregional magnetic resonance imaging (MRI) radiomics features and clinical factors to predict tumor deposits (TDs) preoperatively in resectable rectal cancer (RC).

Methods

This study retrospectively included 148 resectable RC patients [TDs⁺ (n = 45); TDs⁻ (n = 103)] from August 2016 to August 2022, who were divided randomly into a testing cohort (n = 45) and a training cohort (n = 103). Radiomics features were extracted from the volume of interest on T2-weighted images (T2WI) and diffusion-weighted images (DWI) from pretreatment MRI. Model construction was performed after feature selection. Finally, five classification models were developed by support vector machine (SVM) algorithm to predict TDs in resectable RC using the selected clinical factor, single-regional radiomics features (extracted from primary tumor), and multiregional radiomics features (extracted from the primary tumor and mesorectal fat). Receiver-operating characteristic (ROC) curve analysis was employed to assess the discrimination performance of the five models. The AUCs of five models were compared by DeLon’s test.

Results

The training and testing cohorts included 31 (30.1%) and 14 (31.1%) patients with TDs, respectively. The AUCs of multiregional radiomics, single-regional radiomics, and the clinical models for predicting TDs were 0.839, 0.765, and 0.793, respectively. An integrated model incorporating multiregional radiomics features and clinical factors showed good predictive performance for predicting TDs in resectable RC (AUC, 0.931; 95% CI, 0.841–0.988), which demonstrated superiority over clinical model (P = 0.016), the single-regional radiomics model (P = 0.042), and the multiregional radiomics model (P = 0.025).

Conclusion

An integrated model combining multiregional MRI radiomic features and clinical factors can improve prediction performance for TDs and guide clinicians in implementing treatment plans individually for resectable RC patients.

Graphical abstract

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Bray F, Ferlay J, Soerjomataram I, Siegel R L, Torre L A, Jemal A(2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin 68 (6):394-424. https://doi.org/https://doi.org/10.3322/caac.21492CrossRefPubMed

Keum N, Giovannucci E(2019) Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies.Nat Rev Gastroenterol Hepatol 16 (12):713-732. https://doi.org/https://doi.org/10.1038/s41575-019-0189-8CrossRefPubMed

Lin Q, Wei Y, Ren L, Zhong Y, Qin C, Zheng P, Xu P, Zhu D, Ji M, Xu J(2015) Tumor deposit is a poor prognostic indicator in patients who underwent simultaneous resection for synchronous colorectal liver metastases.Onco Targets Ther 8233-240. https://doi.org/https://doi.org/10.2147/ott.S71414CrossRefPubMedPubMedCentral

Lord A C, D’Souza N, Pucher P H, Moran B J, Abulafi A M, Wotherspoon A, Rasheed S, Brown G(2017) Significance of extranodal tumour deposits in colorectal cancer: A systematic review and meta-analysis.Eur J Cancer 8292-102. https://doi.org/https://doi.org/10.1016/j.ejca.2017.05.027CrossRefPubMed

Ueno H, Hashiguchi Y, Shimazaki H, Shinto E, Kajiwara Y, Nakanishi K, Kato K, Maekawa K, Nakamura T, Yamamoto J, Hase K (2014) Peritumoral deposits as an adverse prognostic indicator of colorectal cancer.Am J Surg 207 (1):70-77. https://doi.org/https://doi.org/10.1016/j.amjsurg.2013.04.009CrossRefPubMed

Diagnosis, Treatment Guidelines For Colorectal Cancer Working Group C(2019) Chinese Society of Clinical Oncology (CSCO) diagnosis and treatment guidelines for colorectal cancer 2018 (English version).Chin J Cancer Res 31 (1):117-134. https://doi.org/https://doi.org/10.21147/j.issn.1000-9604.2019.01.07CrossRef

Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, Rödel C, Cervantes A, Arnold D(2018) Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.Ann Oncol 29 (Suppl 4):iv263. https://doi.org/https://doi.org/10.1093/annonc/mdy161CrossRefPubMed

Hashiguchi Y, Muro K, Saito Y, Ito Y, Ajioka Y, Hamaguchi T, Hasegawa K, Hotta K, Ishida H, Ishiguro M, Ishihara S, Kanemitsu Y, Kinugasa Y, Murofushi K, Nakajima T E, Oka S, Tanaka T, Taniguchi H, Tsuji A, Uehara K, Ueno H, Yamanaka T, Yamazaki K, Yoshida M, Yoshino T, Itabashi M, Sakamaki K, Sano K, Shimada Y, Tanaka S, Uetake H, Yamaguchi S, Yamaguchi N, Kobayashi H, Matsuda K, Kotake K, Sugihara K(2020) Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer.Int J Clin Oncol 25 (1):1-42. https://doi.org/https://doi.org/10.1007/s10147-019-01485-zCrossRefPubMed

Jin M, Frankel W L (2018) Lymph Node Metastasis in Colorectal Cancer.Surg Oncol Clin N Am 27 (2):401-412. https://doi.org/https://doi.org/10.1016/j.soc.2017.11.011CrossRefPubMed

10.

Pricolo V E, Steingrimsson J, McDuffie T J, McHale J M, McMillen B, Shparber M(2020) Tumor Deposits in Stage III Colon Cancer: Correlation With Other Histopathologic Variables, Prognostic Value, and Risk Stratification-Time to Consider “N2c”.Am J Clin Oncol 43 (2):133-138. https://doi.org/https://doi.org/10.1097/coc.0000000000000645CrossRefPubMed

11.

Washington M K, Berlin J, Branton P, Burgart L J, Carter D K, Fitzgibbons P L, Halling K, Frankel W, Jessup J, Kakar S, Minsky B, Nakhleh R, Compton C C(2009) Protocol for the examination of specimens from patients with primary carcinoma of the colon and rectum.Arch Pathol Lab Med 133 (10):1539-1551. https://doi.org/https://doi.org/10.5858/133.10.1539CrossRefPubMedPubMedCentral

12.

Nagtegaal I D, Knijn N, Hugen N, Marshall H C, Sugihara K, Tot T, Ueno H, Quirke P(2017) Tumor Deposits in Colorectal Cancer: Improving the Value of Modern Staging-A Systematic Review and Meta-Analysis.J Clin Oncol 35 (10):1119-1127. https://doi.org/https://doi.org/10.1200/jco.2016.68.9091CrossRefPubMed

13.

Bouquot M, Creavin B, Goasguen N, Chafai N, Tiret E, André T, Flejou J F, Parc Y, Lefevre J H, Svrcek M(2018) Prognostic value and characteristics of N1c colorectal cancer.Colorectal Dis 20 (9):O248-o255. https://doi.org/https://doi.org/10.1111/codi.14289CrossRefPubMed

14.

Yagi R, Shimada Y, Kameyama H, Tajima Y, Okamura T, Sakata J, Kobayashi T, Kosugi S I, Wakai T, Nogami H, Maruyama S, Takii Y, Kawasaki T, Honma K I (2016) Clinical Significance of Extramural Tumor Deposits in the Lateral Pelvic Lymph Node Area in Low Rectal Cancer: A Retrospective Study at Two Institutions.Ann Surg Oncol 23 (Suppl 4):552-558. https://doi.org/https://doi.org/10.1245/s10434-016-5379-9CrossRefPubMedPubMedCentral

15.

Weiser M R (2018) AJCC 8th Edition: Colorectal Cancer.Ann Surg Oncol 25 (6):1454-1455. https://doi.org/https://doi.org/10.1245/s10434-018-6462-1CrossRefPubMed

16.

Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, Rödel C, Cervantes A, Arnold D(2017) Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.Ann Oncol 28 (suppl_4):iv22-iv40. https://doi.org/https://doi.org/10.1093/annonc/mdx224CrossRefPubMed

17.

Nougaret S, Jhaveri K, Kassam Z, Lall C, Kim D H(2019) Rectal cancer MR staging: pearls and pitfalls at baseline examination.Abdom Radiol (NY) 44 (11):3536-3548. https://doi.org/https://doi.org/10.1007/s00261-019-02024-0CrossRef

18.

Fernandes M C, Gollub M J, Brown G(2022) The importance of MRI for rectal cancer evaluation.Surg Oncol 43101739. https://doi.org/https://doi.org/10.1016/j.suronc.2022.101739CrossRefPubMedPubMedCentral

19.

Hoshino N, Murakami K, Hida K, Sakamoto T, Sakai Y(2019) Diagnostic accuracy of magnetic resonance imaging and computed tomography for lateral lymph node metastasis in rectal cancer: a systematic review and meta-analysis.Int J Clin Oncol 24 (1):46-52. https://doi.org/https://doi.org/10.1007/s10147-018-1349-5CrossRefPubMed

20.

Li X T, Sun Y S, Tang L, Cao K, Zhang X Y(2015) Evaluating local lymph node metastasis with magnetic resonance imaging, endoluminal ultrasound and computed tomography in rectal cancer: a meta-analysis.Colorectal Dis 17 (6):O129-135. https://doi.org/https://doi.org/10.1111/codi.12909CrossRefPubMed

21.

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout R G, Granton P, Zegers C M, Gillies R, Boellard R, Dekker A, Aerts H J(2012) Radiomics: extracting more information from medical images using advanced feature analysis.Eur J Cancer 48 (4):441-446. https://doi.org/https://doi.org/10.1016/j.ejca.2011.11.036CrossRefPubMedPubMedCentral

22.

Antunes J T, Ofshteyn A, Bera K, Wang E Y, Brady J T, Willis J E, Friedman K A, Marderstein E L, Kalady M F, Stein S L, Purysko A S, Paspulati R, Gollamudi J, Madabhushi A, Viswanath S E(2020) Radiomic Features of Primary Rectal Cancers on Baseline T (2) -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study.J Magn Reson Imaging 52 (5):1531-1541. https://doi.org/https://doi.org/10.1002/jmri.27140CrossRefPubMedPubMedCentral

23.

Bi W L, Hosny A, Schabath M B, Giger M L, Birkbak N J, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn I F, Mak R H, Tamimi R M, Tempany C M, Swanton C, Hoffmann U, Schwartz L H, Gillies R J, Huang R Y, Aerts H(2019) Artificial intelligence in cancer imaging: Clinical challenges and applications.CA Cancer J Clin 69 (2):127-157. https://doi.org/https://doi.org/10.3322/caac.21552CrossRefPubMedPubMedCentral

24.

Gillies R J, Kinahan P E, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data.Radiology 278 (2):563-577. https://doi.org/https://doi.org/10.1148/radiol.2015151169CrossRefPubMed

25.

Atre I D, Eurboonyanun K, Noda Y, Parakh A, O’Shea A, Lahoud R M, Sell N M, Kunitake H, Harisinghani M G(2021) Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort.Abdom Radiol (NY) 46 (2):459-468. https://doi.org/https://doi.org/10.1007/s00261-020-02653-wCrossRef

26.

Chen L D, Li W, Xian M F, Zheng X, Lin Y, Liu B X, Lin M X, Li X, Zheng Y L, Xie X Y, Lu M D, Kuang M, Xu J B, Wang W(2020) Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.Eur Radiol 30 (4):1969-1979. https://doi.org/https://doi.org/10.1007/s00330-019-06558-1CrossRefPubMed

27.

Jin Y, Li M, Zhao Y, Huang C, Liu S, Liu S, Wu M, Song B(2021) Computed Tomography-Based Radiomics for Preoperative Prediction of Tumor Deposits in Rectal Cancer.Front Oncol 11710248. https://doi.org/https://doi.org/10.3389/fonc.2021.710248CrossRefPubMedPubMedCentral

28.

Yang Y S, Feng F, Qiu Y J, Zheng G H, Ge Y Q, Wang Y T(2021) High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer.Abdom Radiol (NY) 46 (3):873-884. https://doi.org/https://doi.org/10.1007/s00261-020-02733-xCrossRef

29.

Jayaprakasam V S, Paroder V, Gibbs P, Bajwa R, Gangai N, Sosa R E, Petkovska I, Golia Pernicka J S, Fuqua J L, 3rd, Bates D D B, Weiser M R, Cercek A, Gollub M J(2022) MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer.Eur Radiol 32 (2):971-980. https://doi.org/https://doi.org/10.1007/s00330-021-08144-wCrossRefPubMed

30.

Liu X, Yang Q, Zhang C, Sun J, He K, Xie Y, Zhang Y, Fu Y, Zhang H(2020) Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer.Front Oncol 10585767. https://doi.org/https://doi.org/10.3389/fonc.2020.585767CrossRefPubMedPubMedCentral

31.

Shaish H, Aukerman A, Vanguri R, Spinelli A, Armenta P, Jambawalikar S, Makkar J, Bentley-Hibbert S, Del Portillo A, Kiran R, Monti L, Bonifacio C, Kirienko M, Gardner K L, Schwartz L, Keller D(2020) Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.Eur Radiol 30 (11):6263-6273. https://doi.org/https://doi.org/10.1007/s00330-020-06968-6CrossRefPubMed

32.

Pan A F, Zheng N X, Wang J, Kabemba J L T, Zheng K, Shen F, Zhang W, Gao X H(2022) Role of Perirectal Fat in the Carcinogenesis and Development of Early-Onset Rectal Cancer.J Oncol 20224061142. https://doi.org/https://doi.org/10.1155/2022/4061142CrossRefPubMedPubMedCentral

33.

Song Y, Zhang J, Zhang Y D, Hou Y, Yan X, Wang Y, Zhou M, Yao Y F, Yang G(2020) FeAture Explorer (FAE): A tool for developing and comparing radiomics models.PLoS One 15 (8):e0237587. https://doi.org/https://doi.org/10.1371/journal.pone.0237587CrossRefPubMedPubMedCentral

34.

van Griethuysen J J M, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan R G H, Fillion-Robin J C, Pieper S, Aerts H (2017) Computational Radiomics System to Decode the Radiographic Phenotype.Cancer Res 77 (21):e104-e107. https://doi.org/https://doi.org/10.1158/0008-5472.Can-17-0339CrossRefPubMedPubMedCentral

35.

Gopal P, Lu P, Ayers G D, Herline A J, Washington M K(2014) Tumor deposits in rectal adenocarcinoma after neoadjuvant chemoradiation are associated with poor prognosis.Mod Pathol 27 (9):1281-1287. https://doi.org/https://doi.org/10.1038/modpathol.2013.239CrossRefPubMedPubMedCentral

36.

Ng F, Kozarski R, Ganeshan B, Goh V(2013) Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?Eur J Radiol 82 (2):342-348. https://doi.org/https://doi.org/10.1016/j.ejrad.2012.10.023CrossRefPubMed

37.

Braman N M, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A(2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.Breast Cancer Res 19 (1):57. https://doi.org/https://doi.org/10.1186/s13058-017-0846-1CrossRefPubMedPubMedCentral

38.

Kickingereder P, Bonekamp D, Nowosielski M, Kratz A, Sill M, Burth S, Wick A, Eidel O, Schlemmer H P, Radbruch A, Debus J, Herold-Mende C, Unterberg A, Jones D, Pfister S, Wick W, von Deimling A, Bendszus M, Capper D (2016) Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.Radiology 281 (3):907-918. https://doi.org/https://doi.org/10.1148/radiol.2016161382CrossRefPubMed

39.

Li Z C, Bai H, Sun Q, Li Q, Liu L, Zou Y, Chen Y, Liang C, Zheng H(2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.Eur Radiol 28 (9):3640-3650. https://doi.org/https://doi.org/10.1007/s00330-017-5302-1CrossRefPubMed

Titel: Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer
verfasst von: Feiwen Feng
Yuanqing Liu
Jiayi Bao
Rong Hong
Su Hu
Chunhong Hu
Publikationsdatum: 14.08.2023
Verlag: Springer US
Erschienen in: Abdominal Radiology / Ausgabe 11/2023
Print ISSN: 2366-004X
Elektronische ISSN: 2366-0058
DOI: https://doi.org/10.1007/s00261-023-04013-w

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Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer