|
1
|
Gabriel E, Ostapoff K, Attwood K,
Al-Sukhni E, Boland P and Nurkin S: Disparities in the age-related
rates of colorectal cancer in the United States. Am Surg.
83:640–647. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Glynne-Jones R, Wyrwicz L, Tiret E, Brown
G, Rödel C, Cervantes A and Arnold D; ESMO Guidelines Committee, :
Rectal cancer: ESMO clinical practice guidelines for diagnosis,
treatment and follow-up. Ann Oncol. 28 (Suppl_4):iv22–iv40. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Siegel RL, Miller KD, Goding Sauer A,
Fedewa SA, Butterly LF, Anderson JC, Cercek A, Smith RA and Jemal
A: Colorectal cancer statistics, 2020. CA Cancer J Clin.
70:145–164. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Ferlay J, Colombet M, Soerjomataram I,
Dyba T, Randi G, Bettio M, Gavin A, Visser O and Bray F: Cancer
incidence and mortality patterns in Europe: Estimates for 40
countries and 25 major cancers in 2018. Eur J Cancer. 103:356–387.
2018. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Bigness A, Imanirad I, Sahin IH, Xie H,
Frakes J, Hoffe S, Laskowitz D and Felder S: Locally advanced
rectal adenocarcinoma: Treatment sequences, intensification, and
rectal organ preservation. CA Cancer J Clin. 71:198–208. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Wei J, Huang R, Guo S, Zhang X, Xi S, Wang
Q, Chang H, Wang X, Xiao W, Zeng Z and Gao Y: ypTNM category
combined with AJCC tumor regression grade for screening patients
with the worst prognosis after neoadjuvant chemoradiation therapy
for locally advanced rectal cancer. Cancer Manag Res. 10:5219–5225.
2018. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Edge SB and Compton CC: The American Joint
Committee on Cancer: The 7th edition of the AJCC cancer staging
manual and the future of TNM. Ann Surg Oncol. 17:1471–1474. 2010.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Cercek A, Roxburgh CSD, Strombom P, Smith
JJ, Temple LKF, Nash GM, Guillem JG, Paty PB, Yaeger R, Stadler ZK,
et al: Adoption of total neoadjuvant therapy for locally advanced
rectal cancer. JAMA Oncol. 4:e1800712018. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Conroy T, Bosset JF, Etienne PL, Rio E,
François É, Mesgouez-Nebout N, Vendrely V, Artignan X, Bouché O,
Gargot D, et al: Neoadjuvant chemotherapy with FOLFIRINOX and
preoperative chemoradiotherapy for patients with locally advanced
rectal cancer (UNICANCER-PRODIGE 23): A multicentre, randomised,
open-label, phase 3 trial. Lancet Oncol. 22:702–715. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Li Y, Wang J, Ma XW, Tan L, Yan Y, Xue C,
Hui B, Liu R, Ma H and Ren J: A Review of neoadjuvant
chemoradiotherapy for locally advanced rectal cancer. Int J Biol
Sci. 12:1022–1031. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Cunningham D, Atkin W, Lenz HJ, Lynch HT,
Minsky B, Nordlinger B and Starling N: Colorectal cancer. Lancet.
375:1030–1047. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Roh MS, Colangelo LH, O'Connell MJ,
Yothers G, Deutsch M, Allegra CJ, Kahlenberg MS, Baez-Diaz L,
Ursiny CS, Petrelli NJ and Wolmark N: Preoperative multimodality
therapy improves disease-free survival in patients with carcinoma
of the rectum: NSABP R-03. J Clin Oncol. 27:5124–5130. 2009.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Sun Z, Adam MA, Kim J, Turner MC, Fisher
DA, Choudhury KR, Czito BG, Migaly J and Mantyh CR: Association
between neoadjuvant chemoradiation and survival for patients with
locally advanced rectal cancer. Colorectal Dis. 19:1058–1066. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Park IJ, You YN, Agarwal A, Skibber JM,
Rodriguez-Bigas MA, Eng C, Feig BW, Das P, Krishnan S, Crane CH, et
al: Neoadjuvant treatment response as an early response indicator
for patients with rectal cancer. J Clin Oncol. 30:1770–1776. 2012.
View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Ravenda P, Gregato G, Rotundo M, Frassoni
S, Dell'Acqua V, Trovato C, Petz W, Rafaniello Raviele P, Bagnardi
V, Bertolini F, et al: Predictive value of circulating
tumor-derived DNA (ctDNA) in patients with locally advanced rectal
cancer (LARC) treated with neoadjuvant chemoradiotherapy (CT-RT):
Preliminary results. Ann Oncol. 29:V852018. View Article : Google Scholar
|
|
16
|
do Canto LM, Barros-Filho MC, Rainho CA,
Marinho D, Kupper BEC, Begnami MDFS, Scapulatempo-Neto C, Havelund
BM, Lindebjerg J, Marchi FA, et al: Comprehensive Analysis of DNA
methylation and prediction of response to Neoadjuvanttherapy in
locally advanced rectal cancer. Cancers (Basel). 12:30792020.
View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Timudom K, Akaraviputh T,
Chinswangwatanakul V, Pongpaibul A, Korpraphong P, Petsuksiri J,
Ithimakin S and Trakarnsanga A: Predictive significance of cancer
related-inflammatory markers in locally advanced rectal cancer.
World J Gastrointest Surg. 12:390–396. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Vuijk FA, van de Water C, Lent-van Vliet
S, van der Valk MJM, Simmer F, van de Velde CJH, Vahrmeijer AL,
Nagtegaal ID and Hilling DE: Intra-Tumoral genomic heterogeneity in
rectal cancer: Mutational status is dependent on preoperative
biopsy depth and location. Cancers (Basel). 13:22712021. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Davnall F, Yip CS, Ljungqvist G, Selmi M,
Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ and Goh V:
Assessment of tumor heterogeneity: An emerging imaging tool for
clinical practice? Insights Imaging. 3:573–589. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Greenbaum A, Martin DR, Bocklage T, Lee
JH, Ness SA and Rajput A: Tumor heterogeneity as a predictor of
response to neoadjuvant chemotherapy in locally advanced rectal
cancer. Clin Colorectal Cancer. 18:102–109. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Bundschuh RA, Dinges J, Neumann L,
Seyfried M, Zsótér N, Papp L, Rosenberg R, Becker K, Astner ST,
Henninger M, et al: Textural parameters of tumor heterogeneity in
18F-FDG PET/CT for therapy response assessment and
prognosis in patients with locally advanced rectal cancer. J Nucl
Med. 55:891–897. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Lambin P, Rios-Velazquez E, Leijenaar R,
Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R,
Boellard R, Dekker A and Aerts HJ: Radiomics: Extracting more
information from medical images using advanced feature analysis.
Eur J Cancer. 48:441–446. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Aerts HJ, Velazquez ER, Leijenaar RT,
Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R,
Haibe-Kains B, Rietveld D, et al: Decoding tumour phenotype by
noninvasive imaging using a quantitative radiomics approach. Nat
Commun. 5:40062014. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Gillies RJ, Kinahan PE and Hricak H:
Radiomics: Images are more than pictures, they are data. Radiology.
278:563–577. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Kiessling F: The changing face of cancer
diagnosis: From computational image analysis to systems biology.
Eur Radiol. 28:3160–3164. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Lambin P, Leijenaar RTH, Deist TM,
Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM,
Even AJG, Jochems A, et al: Radiomics: The bridge between medical
imaging and personalized medicine. Nat Rev Clin Oncol. 14:749–762.
2017. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Bi WL, Hosny A, Schabath MB, Giger ML,
Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, et
al: Artificial intelligence in cancer imaging: Clinical challenges
and applications. CA Cancer J Clin. 69:127–157. 2019.PubMed/NCBI
|
|
28
|
Larue RT, Defraene G, De Ruysscher D,
Lambin P and van Elmpt W: Quantitative radiomics studies for tissue
characterization: A review of technology and methodological
procedures. Br J Radiol. 90:201606652017. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Liang C, Cheng Z, Huang Y, He L, Chen X,
Ma Z, Huang X, Liang C and Liu Z: An MRI-based radiomics classifier
for preoperative prediction of Ki-67 status in breast cancer. Acad
Radiol. 25:1111–1117. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Bulens P, Couwenberg A, Intven M,
Debucquoy A, Vandecaveye V, Van Cutsem E, D'Hoore A, Wolthuis A,
Mukherjee P, Gevaert O and Haustermans K: Predicting the tumor
response to chemoradiotherapy for rectal cancer: Model development
and external validation using MRI radiomics. Radiother Oncol.
142:246–252. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Zhang Z, Shen L, Wang Y, Wang J, Zhang H,
Xia F, Wan J and Zhang Z: MRI Radiomics signature as a potential
biomarker for predicting KRAS status in locally advanced rectal
cancer patients. Front Oncol. 11:6140522021. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Petresc B, Lebovici A, Caraiani C, Feier
DS, Graur F and Buruian MM: Pre-treatment T2-WI based radiomics
features for prediction of locally advanced rectal cancer
non-response to Neoadjuvant chemoradiotherapy: A preliminary study.
Cancers (Basel). 12:18942020. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Chen Q, Xia T, Zhang M, Xia N, Liu J and
Yang Y: Radiomics in stroke neuroimaging: Techniques, applications,
and challenges. Aging Dis. 12:143–154. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Fornacon-Wood I, Faivre-Finn C, O'Connor
JPB and Price GJ: Radiomics as a personalized medicine tool in lung
cancer: Separating the hope from the hype. Lung Cancer.
146:197–208. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Gardin I, Gregoire V, Gibon D, Kirisli H,
Pasquier D, Thariat J and Vera P: Radiomics: Principles and
radiotherapy applications. Crit Rev Oncol Hematol. 138:44–50. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Bogowicz M, Vuong D, Huellner MW, Pavic M,
Andratschke N, Gabrys HS, Guckenberger M and Tanadini-Lang S: CT
radiomics and PET radiomics: Ready for clinical implementation? Q J
Nucl Med Mol Imaging. 63:355–370. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Hood L and Friend SH: Predictive,
personalized, preventive, participatory (P4) cancer medicine. Nat
Rev Clin Oncol. 8:184–187. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Pickhardt PJ: Recent developments in
colorectal imaging. Curr Opin Gastroenterol. 31:76–80. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Rutegard MK, Batsman M, Axelsson J,
Brynolfsson P, Brännström F, Rutegård J, Ljuslinder I, Blomqvist L,
Palmqvist R, Rutegård M and Riklund K: PET/MRI and PET/CT hybrid
imaging of rectal cancer-description and initial observations from
the RECTOPET (REctal Cancer trial on PET/MRI/CT) study. Cancer
Imaging. 19:522019. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Raman SP, Chen Y and Fishman EK: Evolution
of imaging in rectal cancer: Multimodality imaging with MDCT, MRI,
and PET. J Gastrointest Oncol. 6:172–184. 2015.PubMed/NCBI
|
|
41
|
Gal O, Feldman D, Mari A, Baker FA, Hebron
D and Kopelman Y: Computerized tomography criteria as a tool for
simplifying the assessment of locally advanced rectal cancer. J
Gastrointest Cancer. 51:130–134. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Rymer B, Curtis NJ, Siddiqui MR and Chand
M: FDG PET/CT can assess the response of locally advanced rectal
cancer to Neoadjuvant chemoradiotherapy: Evidence from
meta-analysis and systematic review. Clin Nucl Med. 41:371–375.
2016. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Avallone A, Aloj L, Pecori B, Caracò C, De
Stefano A, Tatangelo F, Silvestro L, Granata V, Bianco F, Romano C,
et al: 18F-FDG PET/CT is an early predictor of
pathologic tumor response and survival after preoperative
radiochemotherapy with bevacizumab in high-risk locally advanced
rectal cancer. J Nucl Med. 60:1560–1568. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Schmidt G: Importance of whole body MRI
for staging of colorectal cancer. Radiologe. 52:537–544. 2012.(In
German). View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Georgiou PA, Tekkis PP, Constantinides VA,
Patel U, Goldin RD, Darzi AW, John Nicholls R and Brown G:
Diagnostic accuracy and value of magnetic resonance imaging (MRI)
in planning exenterative pelvic surgery for advanced colorectal
cancer. Eur J Cancer. 49:72–81. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Jhaveri KS and Hosseini-Nik H: MRI of
rectal cancer: An overview and update on recent advances. AJR Am J
Roentgenol. 205:W42–W55. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Klessen C, Rogalla P and Taupitz M: Local
staging of rectal cancer: The current role of MRI. Eur Radiol.
17:379–389. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Attenberger UI, Pilz LR, Morelli JN,
Hausmann D, Doyon F, Hofheinz R, Kienle P, Post S, Michaely HJ,
Schoenberg SO and Dinter DJ: Multi-parametric MRI of rectal
cancer-do quantitative functional MR measurements correlate with
radiologic and pathologic tumor stages? Eur J Radiol. 83:1036–1043.
2014. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Xu Q, Xu Y, Sun H, Jiang T, Xie S, Ooi BY
and Ding Y: MRI evaluation of complete response of locally advanced
rectal cancer after Neoadjuvant therapy: Current status and future
trends. Cancer Manag Res. 13:4317–4328. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Yi X, Pei Q, Zhang Y, Zhu H, Wang Z, Chen
C, Li Q, Long X, Tan F, Zhou Z, et al: MRI-Based radiomics predicts
tumor response to neoadjuvant chemoradiotherapy in locally advanced
rectal cancer. Front Oncol. 9:5522019. View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Mirbagheri N, Kumar B, Deb S, Poh BR, Dark
JG, Leow CC and Teoh WM: Lymph node status as a prognostic
indicator after preoperative Neoadjuvant chemoradiotherapy of
rectal cancer. Colorectal Dis. 16:O339–O346. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Cai C, Hu T, Gong J, Huang D, Liu F, Fu C
and Tong T: Multiparametric MRI-based radiomics signature for
preoperative estimation of tumor-stroma ratio in rectal cancer. Eur
Radiol. 31:3326–3335. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
De Cecco CN, Ganeshan B, Ciolina M, Rengo
M, Meinel FG, Musio D, De Felice F, Raffetto N, Tombolini V and
Laghi A: Texture analysis as imaging biomarker of tumoral response
to Neoadjuvant chemoradiotherapy in rectal cancer patients studied
with 3-T magnetic resonance. Invest Radiol. 50:239–245. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Cui Y, Yang W, Ren J, Li D, Du X, Zhang J
and Yang X: Prognostic value of multiparametric MRI-based radiomics
model: Potential role for chemotherapeutic benefits in locally
advanced rectal cancer. Radiother Oncol. 154:161–169. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Kapiteijn E, Marijnen CA, Nagtegaal ID,
Putter H, Steup WH, Wiggers T, Rutten HJ, Pahlman L, Glimelius B,
van Krieken JH, et al: Preoperative radiotherapy combined with
total mesorectal excision for resectable rectal cancer. N Engl J
Med. 345:638–646. 2001. View Article : Google Scholar : PubMed/NCBI
|
|
56
|
Chetty R, Gill P, Govender D, Bateman A,
Chang HJ, Deshpande V, Driman D, Gomez M, Greywoode G, Jaynes E, et
al: International study group on rectal cancer regression grading:
Interobserver variability with commonly used regression grading
systems. Hum Pathol. 43:1917–1923. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Bahadoer RR, Dijkstra EA, van Etten B,
Marijnen CAM, Putter H, Kranenbarg EM, Roodvoets AGH, Nagtegaal ID,
Beets-Tan RGH, Blomqvist LK, et al: Short-course radiotherapy
followed by chemotherapy before total mesorectal excision (TME)
versus preoperative chemoradiotherapy, TME, and optional adjuvant
chemotherapy in locally advanced rectal cancer (RAPIDO): A
randomised, open-label, phase 3 trial. Lancet Oncol. 22:29–42.
2021. View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Smith JJ, Strombom P, Chow OS, Roxburgh
CS, Lynn P, Eaton A, Widmar M, Ganesh K, Yaeger R, Cercek A, et al:
Assessment of a watch-and-wait strategy for rectal cancer in
patients with a complete response after Neoadjuvant therapy. JAMA
Oncol. 5:e1858962019. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Maas M, Nelemans PJ, Valentini V, Das P,
Rödel C, Kuo LJ, Calvo FA, García-Aguilar J, Glynne-Jones R,
Haustermans K, 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.
11:835–844. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Dinapoli N, Barbaro B, Gatta R, Chiloiro
G, Casà C, Masciocchi C, Damiani A, Boldrini L, Gambacorta MA,
Dezio M, et al: Magnetic resonance, vendor-independent, intensity
histogram analysis predicting pathologic complete response after
radiochemotherapy of rectal cancer. Int J Radiat Oncol Biol Phys.
102:765–774. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
61
|
Li Y, Liu W, Pei Q, Zhao L, Güngör C, Zhu
H, Song X, Li C, Zhou Z, Xu Y, et al: Predicting pathological
complete response by comparing MRI-based radiomics pre- and
postneoadjuvant radiotherapy for locally advanced rectal cancer.
Cancer Med. 8:7244–7252. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
Zhou X, Yi Y, Liu Z, Cao W, Lai B, Sun K,
Li L, Zhou Z, Feng Y and Tian J: Radiomics-Based pretherapeutic
prediction of non-response to Neoadjuvant therapy in locally
advanced rectal cancer. Ann Surg Oncol. 26:1676–1684. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
63
|
Sun Y, Wu X, Lin H, Lu X, Huang Y and Chi
P: Lymph node regression to neoadjuvant chemoradiotherapy in
patients with locally advanced rectal cancer: Prognostic
implication and a predictive model. J Gastrointest Surg.
25:1019–1028. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
64
|
Okada K, Sadahiro S, Suzuki T, Tanaka A,
Saito G, Kamijo A, Akiba T and Kawada S: Effects of
chemoradiotherapy on lymph nodes in patients with rectal
adenocarcinoma: Evaluation of numbers and sizes of retrieved lymph
nodes inside and outside the radiation field. Anticancer Res.
34:4195–4200. 2014.PubMed/NCBI
|
|
65
|
Lee SH, Lee JL, Kim CW, Lee HI, Yu CS and
Kim JC: Oncologic significance of para-aortic lymph node and
inferior mesenteric lymph node metastasis in sigmoid and rectal
adenocarcinoma. Eur J Surg Oncol. 43:2076–2083. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Kang J, Hur H, Min BS, Kim NK and Lee KY:
Prognostic impact of inferior mesenteric artery lymph node
metastasis in colorectal cancer. Ann Surg Oncol. 18:704–710. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Mei SW, Liu Z, Wang Z, Pei W, Wei FZ, Chen
JN, Wang ZJ, Shen HY, Li J, Zhao FQ, et al: Impact factors of lymph
node retrieval on survival in locally advanced rectal cancer with
neoadjuvant therapy. World J Clin Cases. 8:6229–6242. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
68
|
Sartori CA, Sartori A, Vigna S, Occhipinti
R and Baiocchi GL: Urinary and sexual disorders after laparoscopic
TME for rectal cancer in males. J Gastrointest Surg. 15:637–643.
2011. View Article : Google Scholar : PubMed/NCBI
|
|
69
|
Herzog T, Belyaev O, Chromik AM, Weyhe D,
Mueller CA, Munding J, Tannapfel A, Uhl W and Seelig MH: TME
quality in rectal cancer surgery. Eur J Med Res. 15:292–296. 2010.
View Article : Google Scholar : PubMed/NCBI
|
|
70
|
Veenhof AA, Brosens R, Engel AF, van der
Peet DL and Cuesta MA: Risk factors and management of presacral
abscess following total mesorectal excision for rectal cancer. Dig
Surg. 26:317–321. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
71
|
Yuval JB, Thompson HM and Garcia-Aguilar
J: Organ preservation in rectal cancer. J Gastrointest Surg.
24:1880–1888. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
72
|
Zhou X, Yi Y, Liu Z, Zhou Z, Lai B, Sun K,
Li L, Huang L, Feng Y, Cao W and Tian J: Radiomics-based
preoperative prediction of lymph node status following neoadjuvant
therapy in locally advanced rectal cancer. Front Oncol. 10:6042020.
View Article : Google Scholar : PubMed/NCBI
|
|
73
|
Marijnen CA: Organ preservation in rectal
cancer: Have all questions been answered? Lancet Oncol. 16:e13–e22.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
74
|
Langman G, Patel A and Bowley DM: Size and
distribution of lymph nodes in rectal cancer resection specimens.
Dis Colon Rectum. 58:406–414. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
75
|
Yuan Y, Pu H, Chen GW, Chen XL, Liu YS,
Liu H, Wang K and Li H: Diffusion-weighted MR volume and apparent
diffusion coefficient for discriminating lymph node metastases and
good response after chemoradiation therapy in locally advanced
rectal cancer. Eur Radiol. 31:200–211. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
76
|
Newton AD, Li J, Jeganathan AN, Mahmoud
NN, Epstein AJ and Paulson EC: A nomogram to predict lymph node
positivity following Neoadjuvant chemoradiation in locally advanced
rectal cancer. Dis Colon Rectum. 59:710–717. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
77
|
Song L and Yin J: Application of texture
analysis based on sagittal fat-suppression and oblique axial
T2-Weighted magnetic resonance imaging to identify lymph node
invasion status of rectal cancer. Front Oncol. 10:13642020.
View Article : Google Scholar : PubMed/NCBI
|
|
78
|
Lievre A, Bachet JB, Boige V, Cayre A, Le
Corre D, Buc E, Ychou M, Bouché O, Landi B, Louvet C, et al: KRAS
mutations as an independent prognostic factor in patients with
advanced colorectal cancer treated with cetuximab. J Clin Oncol.
26:374–379. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
79
|
Sorich MJ, Wiese MD, Rowland A,
Kichenadasse G, McKinnon RA and Karapetis CS: Extended RAS
mutations and anti-EGFR monoclonal antibody survival benefit in
metastatic colorectal cancer: A meta-analysis of randomized,
controlled trials. Ann Oncol. 26:13–21. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
80
|
George B and Kopetz S: Predictive and
prognostic markers in colorectal cancer. Curr Oncol Rep.
13:206–215. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
81
|
Dai D, Wang Y, Zhu L, Jin H and Wang X:
Prognostic value of KRAS mutation status in colorectal cancer
patients: A population-based competing risk analysis. Peerj.
8:e91492020. View Article : Google Scholar : PubMed/NCBI
|
|
82
|
Zhou P, Goffredo P, Ginader T, Thompson D,
Hrabe J, Gribovskaja-Rupp I, Kapadia M and Hassan I: Impact of KRAS
status on tumor response and survival after Neoadjuvant treatment
of locally advanced rectal cancer. J Surg Oncol. 123:278–285. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
83
|
Gupta AK, Bakanauskas VJ, Cerniglia GJ,
Cheng Y, Bernhard EJ, Muschel RJ and McKenna WG: The Ras radiation
resistance pathway. Cancer Res. 61:4278–4282. 2001.PubMed/NCBI
|
|
84
|
Toulany M, Dittmann K, Kruger M, Baumann M
and Rodemann HP: Radioresistance of K-Ras mutated human tumor cells
is mediated through EGFR-dependent activation of PI3K-AKT pathway.
Radiother Oncol. 76:143–150. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
85
|
Peng J, Lin J, Qiu M, Zhao Y, Deng Y, Shao
J, Ding P, Zhang H, Wan D, Lu Z and Pan Z: Oncogene mutation
profile predicts tumor regression and survival in locally advanced
rectal cancer patients treated with preoperative chemoradiotherapy
and radical surgery. Tumour Biol. 39:10104283177096382017.
View Article : Google Scholar : PubMed/NCBI
|
|
86
|
Coppede F, Lopomo A, Spisni R and Migliore
L: Genetic and epigenetic biomarkers for diagnosis, prognosis and
treatment of colorectal cancer. World J Gastroenterol. 20:943–956.
2014. View Article : Google Scholar : PubMed/NCBI
|
|
87
|
Xu Y, Xu Q, Sun H, Liu T, Shi K and Wang
W: Could IVIM and ADC help in predicting the KRAS status in
patients with rectal cancer? Eur Radiol. 28:3059–3065. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
88
|
Yeo DM, Oh SN, Choi MH, Lee SH, Lee MA and
Jung SE: Histogram analysis of perfusion parameters from dynamic
contrast-enhanced MR imaging with tumor characteristics and
therapeutic response in locally advanced rectal cancer. Biomed Resm
Int. 2018:37243932018.PubMed/NCBI
|
|
89
|
Meng X, Xia W, Xie P, Zhang R, Li W, Wang
M, Xiong F, Liu Y, Fan X, Xie Y, et al: Preoperative radiomic
signature based on multiparametric magnetic resonance imaging for
noninvasive evaluation of biological characteristics in rectal
cancer. Eur Radiol. 29:3200–3209. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
90
|
Xu Y, Xu Q, Ma Y, Duan J, Zhang H, Liu T,
Li L, Sun H, Shi K, Xie S and Wang W: Characterizing MRI features
of rectal cancers with different KRAS status. BMC Cancer.
19:11112019. View Article : Google Scholar : PubMed/NCBI
|
|
91
|
Oh JE, Kim MJ, Lee J, Hur BY, Kim B, Kim
DY, Baek JY, Chang HJ, Park SC, Oh JH, et al: Magnetic
resonance-based texture analysis differentiating KRAS mutation
status in rectal cancer. Cancer Res Treat. 52:51–59. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
92
|
Chiloiro G, Rodriguez-Carnero P, Lenkowicz
J, Casà C, Masciocchi C, Boldrini L, Cusumano D, Dinapoli N,
Meldolesi E, Carano D, et al: Delta radiomics can predict distant
metastasis in locally advanced rectal cancer: The challenge to
personalize the cure. Front Oncol. 10:5950122020. View Article : Google Scholar : PubMed/NCBI
|
|
93
|
Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou
X, Sun K, Li L, Li B, Wang M and Tian J: The applications of
radiomics in precision diagnosis and treatment of oncology:
Opportunities and challenges. Theranostics. 9:1303–1322. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
94
|
Mayerhoefer ME, Szomolanyi P, Jirak D,
Materka A and Trattnig S: Effects of MRI acquisition parameter
variations and protocol heterogeneity on the results of texture
analysis and pattern discrimination: An application-oriented study.
Med Phys. 36:1236–1243. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
95
|
Collewet G, Strzelecki M and Mariette F:
Influence of MRI acquisition protocols and image intensity
normalization methods on texture classification. Magn Reson
Imaging. 22:81–91. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
96
|
Daye D, Tabari A, Kim H, Chang K, Kamran
SC, Hong TS, Kalpathy-Cramer J and Gee MS: Quantitative tumor
heterogeneity MRI profiling improves machine learning-based
prognostication in patients with metastatic colon cancer. Eur
Radiol. 31:5759–5767. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
97
|
Li ZY, Wang XD, Li M, Liu XJ, Ye Z, Song
B, Yuan F, Yuan Y, Xia CC, Zhang X and Li Q: Multi-modal radiomics
model to predict treatment response to neoadjuvant chemotherapy for
locally advanced rectal cancer. World J Gastroenterol.
26:2388–2402. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
98
|
Giannini V, Mazzetti S, Bertotto I,
Chiarenza C, Cauda S, Delmastro E, Bracco C, Di Dia A, Leone F,
Medico E, et al: Predicting locally advanced rectal cancer response
to neoadjuvant therapy with 18F-FDG PET and MRI
radiomics features. Eur J Nucl Med Mol Imaging. 46:878–888. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
99
|
Cui Y, Yang X, Shi Z, Yang Z, Du X, Zhao Z
and Cheng X: Radiomics analysis of multiparametric MRI for
prediction of pathological complete response to neoadjuvant
chemoradiotherapy in locally advanced rectal cancer. Eur Radiol.
29:1211–1220. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
100
|
Liu X, Yang Q, Zhang C, Sun J, He K, Xie
Y, Zhang Y, Fu Y and Zhang H: Multiregional-Based magnetic
resonance imaging radiomics combined with clinical data improves
efficacy in predicting lymph node metastasis of rectal cancer.
Front Oncol. 10:5857672021. View Article : Google Scholar : PubMed/NCBI
|
|
101
|
Chartrand G, Cheng PM, Vorontsov E,
Drozdzal M, Turcotte S, Pal CJ, Kadoury S and Tang A: Deep
learning: A primer for radiologists. Radiographics. 37:2113–2131.
2017. View Article : Google Scholar : PubMed/NCBI
|
|
102
|
Parekh VS and Jacobs MA: Deep learning and
radiomics in precision medicine. Expert Rev Precis Med Drug Dev.
4:59–72. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
103
|
Shi L, Zhang Y, Nie K, Sun X, Niu T, Yue
N, Kwong T, Chang P, Chow D, Chen JH and Su MY: Machine learning
for prediction of chemoradiation therapy response in rectal cancer
using pre-treatment and mid-radiation multi-parametric MRI. Magn
Reson Imaging. 61:33–40. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
104
|
Zhu HT, Zhang XY, Shi YJ, Li XT and Sun
YS: 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.
10:5743372020. View Article : Google Scholar : PubMed/NCBIPubMed/NCBI
|