|
1
|
Siegel RL, Wagle NS, Cercek A, Smith RA
and Jemal A: Colorectal cancer statistics, 2023. CA Cancer J Clin.
73:233–254. 2023.PubMed/NCBI
|
|
2
|
Morgan E, Arnold M, Gini A, Lorenzoni V,
Cabasag CJ, Laversanne M, Vignat J, Ferlay J, Murphy N and Bray F:
Global burden of colorectal cancer in 2020 and 2040: Incidence and
mortality estimates from GLOBOCAN. Gut. 72:338–344. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Staal FCR, van der Reijd DJ, Taghavi M,
Lambregts DMJ, Beets-Tan RGH and Maas M: Radiomics for the
prediction of treatment outcome and survival in patients with
colorectal cancer: A systematic review. Clin Colorectal Cancer.
20:52–71. 2021.PubMed/NCBI
|
|
4
|
Aklilu M and Eng C: The current landscape
of locally advanced rectal cancer. Nat Rev Clin Oncol. 8:649–659.
2011. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Benson AB, Venook AP, Al-Hawary MM, Arain
MA, Chen YJ, Ciombor KK, Cohen S, Cooper HS, Deming D,
Garrido-Laguna I, et al: NCCN guidelines insights: Rectal cancer,
version 6.2020. J Natl Compr Canc Netw. 18:806–815. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
6
|
van der Valk MJM, Hilling DE, Bastiaannet
E, Kranenbarg EMK, Beets GL, Figueiredo NL, Habr-Gama A, Perez RO,
Renehan AG and van de Velde CJH; IWWD Consortium, : Long-term
outcomes of clinical complete responders after neoadjuvant
treatment for rectal cancer in the International watch & wait
database (IWWD): An international multicentre registry study.
Lancet. 391:2537–2545. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Gauci C, Ravindran P, Pillinger S and
Lynch AC: Robotic surgery for multi-visceral resection in locally
advanced colorectal cancer: Techniques, benefits and future
directions. Laparoscopic, Endoscopic and Robotic Surgery.
6:123–126. 2023. View Article : Google Scholar
|
|
8
|
Valentini V, van Stiphout RG, Lammering G,
Gambacorta MA, Barba MC, Bebenek M, Bonnetain F, Bosset JF, Bujko
K, Cionini L, et al: Selection of appropriate end-points (pCR vs
2yDFS) for tailoring treatments with prediction models in locally
advanced rectal cancer. Radiother Oncol. 114:302–309. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Fokas E, Liersch T, Fietkau R, Hohenberger
W, Beissbarth T, Hess C, Becker H, Ghadimi M, Mrak K, Merkel S, et
al: Tumor regression grading after preoperative chemoradiotherapy
for locally advanced rectal carcinoma revisited: Updated results of
the CAO/ARO/AIO-94 trial. J Clin Oncol. 32:1554–1562. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
10
|
van Gijn W, Marijnen CA, Nagtegaal ID,
Kranenbarg EM, Putter H, Wiggers T, Rutten HJ, Påhlman L, Glimelius
B and van de Velde CJ; Dutch Colorectal Cancer Group, :
Preoperative radiotherapy combined with total mesorectal excision
for resectable rectal cancer: 12-year follow-up of the multicentre,
randomised controlled TME trial. Lancet Oncol. 12:575–582. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
11
|
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:iv22–iv40. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Cabezón-Gutiérrez L, Custodio-Cabello S,
Palka-Kotlowska M, Díaz-Pérez D, Mateos-Dominguez M and
Galindo-Jara P: Neoadjuvant immunotherapy for dMMR/MSI-H locally
advanced rectal cancer: The future new standard approach? Eur J
Surg Oncol. 49:323–328. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Cercek A, Lumish M, Sinopoli J, Weiss J,
Shia J, Lamendola-Essel M, El Dika IH, Segal N, Shcherba M,
Sugarman R, et al: PD-1 blockade in mismatch repair-deficient,
locally advanced rectal cancer. N Engl J Med. 386:2363–2376. 2022.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Yang R, Wu T, Yu J, Cai X, Li G, Li X,
Huang W, Zhang Y, Wang Y, Yang X, et al: Locally advanced rectal
cancer with dMMR/MSI-H may be excused from surgery after
neoadjuvant anti-PD-1 monotherapy: A multiple-center, cohort study.
Front Immunol. 14:11822992023. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
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
|
|
16
|
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
|
|
17
|
Zwart WH, Temmink SJD, Hospers GAP,
Marijnen CAM, Putter H, Nagtegaal ID, Blomqvist L, Kranenbarg EM,
Roodvoets AGH, Martling A, et al: Oncological outcomes after a
pathological complete response following total neoadjuvant therapy
or chemoradiotherapy for high-risk locally advanced rectal cancer
in the RAPIDO trial. Eur J Cancer. 204:1140442024. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Conroy T, Castan F, Etienne PL, Rio E,
Mesgouez-Nebout N, Evesque L, Vendrely V, Artignan X, Bouché O,
Gargot D, et al: Total neoadjuvant therapy with mFOLFIRINOX versus
preoperative chemoradiotherapy in patients with locally advanced
rectal cancer: Long-term results of the UNICANCER-PRODIGE 23 trial.
Ann Oncol. 35:873–881. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Dijkstra EA, Nilsson PJ, Hospers GAP,
Bahadoer RR, Kranenbarg EMK, Roodvoets AGH, Putter H, Berglund Å,
Cervantes A, Crolla R, et al: Locoregional failure during and after
short-course radiotherapy followed by chemotherapy and surgery
compared with long-course chemoradiotherapy and surgery: A 5-year
follow-up of the RAPIDO trial. Ann Surg. 278:e766–e772. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Valentini V, van Stiphout RG, Lammering G,
Gambacorta MA, Barba MC, Bebenek M, Bonnetain F, Bosset JF, Bujko
K, Cionini L, et al: Nomograms for predicting local recurrence,
distant metastases, and overall survival for patients with locally
advanced rectal cancer on the basis of European randomized clinical
trials. J Clin Oncol. 29:3163–3172. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Sun Y, Lin H, Lu X, Huang Y, Xu Z, Huang
S, Wang X and Chi P: A nomogram to predict distant metastasis after
neoadjuvant chemoradiotherapy and radical surgery in patients with
locally advanced rectal cancer. J Surg Oncol. 115:462–469. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Merkel S, Weber K, Schellerer V, Göhl J,
Fietkau R, Agaimy A, Hohenberger W and Hermanek P: Prognostic
subdivision of ypT3 rectal tumours according to extension beyond
the muscularis propria. Br J Surg. 101:566–572. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
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
|
|
24
|
Bera K, Katz I and Madabhushi A:
Reimagining T staging through artificial intelligence and machine
learning image processing approaches in digital pathology. JCO Clin
Cancer Inform. 4:1039–1050. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Patel UB, Taylor F, Blomqvist L, George C,
Evans H, Tekkis P, Quirke P, Sebag-Montefiore D, Moran B, Heald R,
et al: Magnetic resonance imaging-detected tumor response for
locally advanced rectal cancer predicts survival outcomes: MERCURY
experience. J Clin Oncol. 29:3753–3760. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
De Mattia E, Polesel J, Mezzalira S,
Palazzari E, Pollesel S, Toffoli G and Cecchin E: Predictive and
prognostic value of oncogene mutations and microsatellite
instability in locally-advanced rectal cancer treated with
neoadjuvant radiation-based therapy: A systematic review and
meta-analysis. Cancers (Basel). 15:14692023. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Watanabe T, Kobunai T, Yamamoto Y, Matsuda
K, Ishihara S, Nozawa K, Iinuma H, Shibuya H and Eshima K:
Heterogeneity of KRAS status may explain the subset of discordant
KRAS status between primary and metastatic colorectal cancer. Dis
Colon Rectum. 54:1170–1178. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Ciocan RA, Ciocan A, Mihăileanu FV, Ursu
CP, Ursu Ș, Bodea C, Cordoș AA, Chiș BA, Al Hajjar N, Dîrzu N and
Dîrzu DS: Metabolic signatures: Pioneering the frontier of rectal
cancer diagnosis and response to neoadjuvant treatment with
biomarkers-a systematic review. Int J Mol Sci. 25:23812024.
View Article : Google Scholar : PubMed/NCBI
|
|
29
|
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
|
|
30
|
Kinkel K, Lu Y, Both M, Warren RS and
Thoeni RF: Detection of hepatic metastases from cancers of the
gastrointestinal tract by using noninvasive imaging methods (US,
CT, MR imaging, PET): A meta-analysis. Radiology. 224:748–756.
2002. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Horvat N, Rocha CC, Oliveira BC, Petkovska
I and Gollub MJ: MRI of rectal cancer: Tumor staging, imaging
techniques, and management. Radiographics. 39:367–387. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Nahas SC, Nahas CS, Marques CF, Ribeiro U
Jr, Cotti GC, Imperiale AR, Capareli FC, Chen AT, Hoff PM and
Cecconello I: Pathologic complete response in rectal cancer: Can we
detect it? Lessons learned from a proposed randomized trial of
watch-and-wait treatment of rectal cancer. Dis Colon Rectum.
59:255–263. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Smith JJ and Garcia-Aguilar J: Advances
and challenges in treatment of locally advanced rectal cancer. J
Clin Oncol. 33:1797–1808. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Park H: Predictive factors for early
distant metastasis after neoadjuvant chemoradiotherapy in locally
advanced rectal cancer. World J Gastrointest Oncol. 13:252–264.
2021. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Joo JI, Lim SW and Oh BY: Prognostic
impact of carcinoembryonic antigen levels in rectal cancer patients
who had received neoadjuvant chemoradiotherapy. Ann Coloproctol.
37:179–185. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Smith N and Brown G: Preoperative staging
of rectal cancer. Acta Oncol. 47:20–31. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Smith NJ, Barbachano Y, Norman AR, Swift
RI, Abulafi AM and Brown G: Prognostic significance of magnetic
resonance imaging-detected extramural vascular invasion in rectal
cancer. Br J Surg. 95:229–236. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Nougaret S, Reinhold C, Mikhael HW,
Rouanet P, Bibeau F and Brown G: The use of MR imaging in treatment
planning for patients with rectal carcinoma: have you checked the
‘DISTANCE’? Radiology. 268:330–344. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Nougaret S, Gormly K, Lambregts DMJ,
Reinhold C, Goh V, Korngold E, Denost Q and Brown G: MRI of the
rectum: A decade into DISTANCE, moving to DISTANCED. Radiology.
314:e2328382025. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Hosny A, Parmar C, Quackenbush J, Schwartz
LH and Aerts H: Artificial intelligence in radiology. Nat Rev
Cancer. 18:500–510. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
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
|
|
42
|
Wang J, Shen L, Zhong H, Zhou Z, Hu P, Gan
J, Luo R, Hu W and Zhang Z: Radiomics features on radiotherapy
treatment planning CT can predict patient survival in locally
advanced rectal cancer patients. Sci Rep. 9:153462019. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
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
|
|
44
|
Bang JI, Ha S, Kang SB, Lee KW, Lee HS,
Kim JS, Oh HK, Lee HY and Kim SE: Prediction of neoadjuvant
radiation chemotherapy response and survival using pretreatment
[(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J
Nucl Med Mol Imaging. 43:422–431. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Page MJ, McKenzie JE, Bossuyt PM, Boutron
I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan
SE, et al: The PRISMA 2020 statement: An updated guideline for
reporting systematic reviews. BMJ. 372:n712021. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
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
|
|
47
|
Scapicchio C, Gabelloni M, Barucci A,
Cioni D, Saba L and Neri E: A deep look into radiomics. Radiol Med.
126:1296–1311. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Floca R, Bohn J, Haux C, Wiestler B,
Zöllner FG, Reinke A, Weiß J, Nolden M, Albert S, Persigehl T, et
al: Radiomics workflow definition & challenges-German priority
program 2177 consensus statement on clinically applied radiomics.
Insights Imaging. 15:1242024. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Lin M, Tang X, Cao L, Liao Y, Zhang Y and
Zhou J: Using ultrasound radiomics analysis to diagnose cervical
lymph node metastasis in patients with nasopharyngeal carcinoma.
Eur Radiol. 33:774–783. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Ghosh A, Yekeler E, Teixeira SR, Dalal D
and States L: Role of MRI radiomics for the prediction of MYCN
amplification in neuroblastomas. Eur Radiol. 33:6726–6735. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Xie F, Zhao Q, Li S, Wu S, Li J, Li H,
Chen S, Jiang W, Dong A, Wu L, et al: Establishment and validation
of novel MRI radiomic feature-based prognostic models to predict
progression-free survival in locally advanced rectal cancer. Front
Oncol. 12:9012872022. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Chiloiro G, Cusumano D, Romano A, Boldrini
L, Nicolì G, Votta C, Tran HE, Barbaro B, Carano D, Valentini V, et
al: Delta radiomic analysis of mesorectum to predict treatment
response and prognosis in locally advanced rectal cancer. Cancers
(Basel). 15:30822023. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Steyerberg EW and Harrell FE Jr:
Prediction models need appropriate internal, internal-external, and
external validation. J Clin Epidemiol. 69:245–247. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Jayaprakasam VS, Paroder V, Gibbs P, Bajwa
R, Gangai N, Sosa RE, Petkovska I, Pernicka JS, Fuqua JL III, Bates
DDB, et al: 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.
32:971–980. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
55
|
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
|
|
56
|
Huang H, Han L, Guo J, Zhang Y, Lin S,
Chen S, Lin X, Cheng C, Guo Z and Qiu Y: Pretreatment MRI-based
radiomics for prediction of rectal cancer outcome: A discovery and
validation study. Acad Radiol. 31:1878–1888. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Liu Z, Meng X, Zhang H, Li Z, Liu J, Sun
K, Meng Y, Dai W, Xie P, Ding Y, et al: Predicting distant
metastasis and chemotherapy benefit in locally advanced rectal
cancer. Nat Commun. 11:43082020. View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Wei Q, Chen L, Hou X, Lin Y, Xie R, Yu X,
Zhang H, Wen Z, Wu Y, Liu X and Chen W: Multiparametric MRI-based
radiomic model for predicting lymph node metastasis after
neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Insights Imaging. 15:1632024. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Tibermacine H, Rouanet P, Sbarra M,
Forghani R, Reinhold C and Nougaret S; GRECCAR Study Group, :
Radiomics modelling in rectal cancer to predict disease-free
survival: Evaluation of different approaches. Br J Surg.
108:1243–1250. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Jalil O, Afaq A, Ganeshan B, Patel UB,
Boone D, Endozo R, Groves A, Sizer B and Arulampalam T: Magnetic
resonance based texture parameters as potential imaging biomarkers
for predicting long-term survival in locally advanced rectal cancer
treated by chemoradiotherapy. Colorectal Dis. 19:349–362. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
61
|
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
|
|
62
|
Jeon SH, Song C, Chie EK, Kim B, Kim YH,
Chang W, Lee YJ, Chung JH, Chung JB, Lee KW, et al: Delta-radiomics
signature predicts treatment outcomes after preoperative
chemoradiotherapy and surgery in rectal cancer. Radiat Oncol.
14:432019. View Article : Google Scholar : PubMed/NCBI
|
|
63
|
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
|
|
64
|
Chiloiro G, Boldrini L, Preziosi F,
Cusumano D, Yadav P, Romano A, Placidi L, Lenkowicz J, Dinapoli N,
Bassetti MF, et al: A predictive model of 2yDFS during MR-Guided RT
Neoadjuvant chemoradiotherapy in locally advanced rectal cancer
patients. Front Oncol. 12:8317122022. View Article : Google Scholar : PubMed/NCBI
|
|
65
|
Park H, Kim KA, Jung JH, Rhie J and Choi
SY: MRI features and texture analysis for the early prediction of
therapeutic response to neoadjuvant chemoradiotherapy and tumor
recurrence of locally advanced rectal cancer. Eur Radiol.
30:4201–4211. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Miles KA, Ganeshan B and Hayball MP: CT
texture analysis using the filtration-histogram method: What do the
measurements mean? Cancer Imaging. 13:400–406. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Hocquelet A, Auriac T, Perier C, Dromain
C, Meyer M, Pinaquy JB, Denys A, Trillaud H, Senneville BD and
Vendrely V: Pre-treatment magnetic resonance-based texture features
as potential imaging biomarkers for predicting event free survival
in anal cancer treated by chemoradiotherapy. Eur Radiol.
28:2801–2811. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
68
|
Wang C, Chen J, Zheng N, Zheng K, Zhou L,
Zhang Q and Zhang W: Predicting the risk of distant metastasis in
patients with locally advanced rectal cancer using model based on
pre-treatment T2WI-based radiomic features plus postoperative
pathological stage. Front Oncol. 13:11095882023. View Article : Google Scholar : PubMed/NCBI
|
|
69
|
Meng Y, Zhang Y, Dong D, Li C, Liang X,
Zhang C, Wan L, Zhao X, Xu K, Zhou C, et al: Novel radiomic
signature as a prognostic biomarker for locally advanced rectal
cancer. J Magn Reson Imaging. 13:doi: 10.1002/jmri.25968. 2018.
|
|
70
|
Trebeschi S, van Griethuysen JJM,
Lambregts DMJ, Lahaye MJ, Parmar C, Bakers FCH, Peters N, Beets-Tan
RGH and Aerts H: Deep learning for fully-automated localization and
segmentation of rectal cancer on multiparametric MR. Sci Rep.
7:53012017. View Article : Google Scholar : PubMed/NCBI
|
|
71
|
Shen D, Wu G and Suk HI: Deep learning in
medical image analysis. Annu Rev Biomed Eng. 19:221–248. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
72
|
Shin J, Seo N, Baek SE, Son NH, Lim JS,
Kim NK, Koom WS and Kim S: MRI radiomics model predicts pathologic
complete response of rectal cancer following chemoradiotherapy.
Radiology. 303:351–358. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
73
|
Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT,
Tang Z, Wang S, Li XT, Tian J and Sun YS: Radiomics analysis for
evaluation of pathological complete response to neoadjuvant
chemoradiotherapy in locally advanced rectal cancer. Clin Cancer
Res. 23:7253–7262. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
74
|
Liu X, Zhang D, Liu Z, Li Z, Xie P, Sun K,
Wei W, Dai W, Tang Z, Ding Y, et al: Deep learning radiomics-based
prediction of distant metastasis in patients with locally advanced
rectal cancer after neoadjuvant chemoradiotherapy: A multicentre
study. EBioMedicine. 69:1034422021. View Article : Google Scholar : PubMed/NCBI
|
|
75
|
Jiang X, Zhao H, Saldanha OL, Nebelung S,
Kuhl C, Amygdalos I, Lang SA, Wu X, Meng X, Truhn D, et al: An mri
deep learning model predicts outcome in rectal cancer. Radiology.
307:e2222232023. View Article : Google Scholar : PubMed/NCBI
|
|
76
|
Zhang S, Cai G, Xie P, Sun C, Li B, Dai W,
Liu X, Qiu Q, Du Y, Li Z, et al: Improving prognosis and assessing
adjuvant chemotherapy benefit in locally advanced rectal cancer
with deep learning for MRI: A retrospective, multi-cohort study.
Radiother Oncol. 188:1098992023. View Article : Google Scholar : PubMed/NCBI
|
|
77
|
Najjar R: Redefining radiology: A review
of artificial intelligence integration in medical imaging.
Diagnostics (Basel). 13:27602023. View Article : Google Scholar : PubMed/NCBI
|
|
78
|
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
|
|
79
|
Shahzadi I, Zwanenburg A, Lattermann A,
Linge A, Baldus C, Peeken JC, Combs SE, Diefenhardt M, Rödel C,
Kirste S, et al: Analysis of MRI and CT-based radiomics features
for personalized treatment in locally advanced rectal cancer and
external validation of published radiomics models. Sci Rep.
12:101922022. View Article : Google Scholar : PubMed/NCBI
|
|
80
|
Zhou B, Khosla A, Lapedriza A, Oliva A and
Torralba A: Learning deep features for discriminative localization.
Computer Vision and Pattern Recognition (cs.CV). 2015.https://doi.org/10.48550/arXiv.1512.04150
|
|
81
|
Kazerooni AF, Kraya A, Rathi KS, Kim MC,
Vossough A, Khalili N, Familiar AM, Gandhi D, Khalili N, Kesherwani
V, et al: Multiparametric MRI along with machine learning predicts
prognosis and treatment response in pediatric low-grade glioma. Nat
Commun. 16:3402025. View Article : Google Scholar
|
|
82
|
Bando H, Ohtsu A and Yoshino T:
Therapeutic landscape and future direction of metastatic colorectal
cancer. Nat Rev Gastroenterol Hepatol. 20:306–322. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
83
|
Dayde D, Tanaka I, Jain R, Tai MC and
Taguchi A: Predictive and prognostic molecular biomarkers for
response to neoadjuvant chemoradiation in rectal cancer. Int J Mol
Sci. 18:5732017. View Article : Google Scholar : PubMed/NCBI
|