|
1
|
Han B, Zheng R, Zeng H, Wang S, Sun K,
Chen R, Li L, Wei W and He J: Cancer incidence and mortality in
China, 2022. J Natl Cancer Cen. 4:47–53. 2024. View Article : Google Scholar
|
|
2
|
Wang Z, Kim SY, Tu W, Kim J, Xu A, Yang
YM, Matsuda M, Reolizo L, Tsuchiya T, Billet S, et al:
Extracellular vesicles in fatty liver promote a metastatic tumor
microenvironment. Cell Metab. 35:1209–1226.e13. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Liang M, Ma X, Wang L, Li D, Wang S, Zhang
H and Zhao X: Whole-liver enhanced CT radiomics analysis to predict
metachronous liver metastases after rectal cancer surgery. Cancer
Imaging. 22:502022. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Horak J, Kubecek O, Siskova A, Honkova K,
Chvojkova I, Krupova M, Manethova M, Vodenkova S, García-Mulero S,
John S, et al: Differences in genome, transcriptome, miRNAome, and
methylome in synchronous and metachronous liver metastasis of
colorectal cancer. Front Oncol. 13:11335982023. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Xu N, Gao Z, Wu D, Chen H, Zhang Z, Zhang
L, Wang Y, Lu X, Yao X, Liu X, et al: 5-hydroxymethylcytosine
features of portal venous blood predict metachronous liver
metastases of colorectal cancer and reveal phosphodiesterase 4 as a
therapeutic target. Clin Transl Med. 15:e701892025. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Trailin A, Ali E, Ye W, Pavlov S,
Červenková L, Vyčítal O, Ambrozkiewicz F, Hošek P, Daum O, Liška V
and Hemminki K: Prognostic assessment of T-cells in primary
colorectal cancer and paired synchronous or metachronous liver
metastasis. Int J Cancer. 156:1282–1292. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Veen T, Kanani A, Zaharia C, Lea D and
Søreide K: Treatment-sequencing before and after index hepatectomy
with either synchronous or metachronous colorectal liver
metastasis: Comparison of recurrence risk, repeat hepatectomy and
overall survival in a population-derived cohort. Eur J Surg Oncol.
51:1095402025. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Inchingolo R, Maino C, Cannella R,
Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M,
Giannini V, et al: Radiomics in colorectal cancer patients. World J
Gastroenterol. 29:2888–2904. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Hao M, Wang K, Ding Y, Li H, Liu Y and
Ding L: Which patients are prone to suffer liver metastasis? A
review of risk factors of metachronous liver metastasis of
colorectal cancer. Eur J Med Res. 27:1302022. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Taghavi M, Trebeschi S, Simões R, Meek DB,
Beckers RCJ, Lambregts DMJ, Verhoef C, Houwers JB, van der Heide
UA, Beets-Tan RGH and Maas M: Machine learning-based analysis of CT
radiomics model for prediction of colorectal metachronous liver
metastases. Abdom Radiol (NY). 46:249–256. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Bera K, Braman N, Gupta A, Velcheti V and
Madabhushi A: Predicting cancer outcomes with radiomics and
artificial intelligence in radiology. Nat Rev Clin Oncol.
19:132–146. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Bo Z, Song J, He Q, Chen B, Chen Z, Xie X,
Shu D, Chen K, Wang Y and Chen G: Application of artificial
intelligence radiomics in the diagnosis, treatment, and prognosis
of hepatocellular carcinoma. Comput Biol Med. 173:1083372024.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Prelaj A, Miskovic V, Zanitti M, Trovo F,
Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S,
et al: Artificial intelligence for predictive biomarker discovery
in immuno-oncology: A systematic review. Ann Oncol. 35:29–65. 2024.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Valladares A, Beyer T, Papp L, Salomon E
and Rausch I: A multi-modality physical phantom for mimicking tumor
heterogeneity patterns in PET/CT and PET/MRI. Med Phys.
49:5819–5829. 2022. View
Article : Google Scholar : PubMed/NCBI
|
|
15
|
Kalisvaart GM, van Velden FHP,
Hernández-Girón I, Meijer KM, Ghesquiere-Dierickx LMH, Brink WM,
Webb A, de Geus-Oei LF, Slump CH, Kuznetsov DV, et al: Design and
evaluation of a modular multimodality imaging phantom to simulate
heterogeneous uptake and enhancement patterns for radiomic
quantification in hybrid imaging: A feasibility study. Med Phys.
49:3093–3106. 2022. View
Article : Google Scholar : PubMed/NCBI
|
|
16
|
Xie Z, Zhang Q, Wang X, Chen Y, Deng Y,
Lin H, Wu J, Huang X, Xu Z and Chi P: Development and validation of
a novel radiomics nomogram for prediction of early recurrence in
colorectal cancer. Eur J Surg Oncol. 49:1071182023. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Luo X, Deng H, Xie F, Wang L, Liang J, Zhu
X, Li T, Tang X, Liang W, Xiang Z and He J: Prognostication of
colorectal cancer liver metastasis by CE-based radiomics and
machine learning. Transl Oncol. 47:1019972024. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
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
|
|
19
|
Vogel JD, Felder SI, Bhama AR, Hawkins AT,
Langenfeld SJ, Shaffer VO, Thorsen AJ, Weiser MR, Chang GJ,
Lightner AL, et al: The American society of colon and rectal
surgeons clinical practice guidelines for the management of colon
cancer. Dis Colon Rectum. 65:148–177. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Katipally RR, Martinez CA, Pugh SA,
Bridgewater JA, Primrose JN, Domingo E, Maughan TS, Talamonti MS,
Posner MC, Weichselbaum RR, et al: Integrated Clinical-molecular
classification of colorectal liver metastases: A biomarker analysis
of the phase 3 new EPOC randomized clinical trial. JAMA Oncol.
9:1245–1254. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Siriwardena AK, Serrablo A, Fretland ÅA,
Wigmore SJ, Ramia-Angel JM, Malik HZ, Stättner S, Søreide K, Zmora
O, Meijerink M, et al: Multisocietal European consensus on the
terminology, diagnosis, and management of patients with synchronous
colorectal cancer and liver metastases: An E-AHPBA consensus in
partnership with ESSO, ESCP, ESGAR, and CIRSE. Br J Surg.
110:1161–1170. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Kim K, Kim S, Han K, Bae H, Shin J and Lim
JS: Diagnostic performance of deep learning-based lesion detection
algorithm in CT for detecting hepatic metastasis from colorectal
cancer. Korean J Radiol. 22:912–921. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Creasy JM, Cunanan KM, Chakraborty J,
McAuliffe JC, Chou J, Gonen M, Kingham VS, Weiser MR, Balachandran
VP, Drebin JA, et al: Differences in liver parenchyma are
measurable with CT radiomics at initial colon resection in patients
that develop hepatic metastases from stage II/III colon cancer. Ann
Surg Oncol. 28:1982–1989. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Chandra P and Sacks GD: Contemporary
Surgical Management of Colorectal Liver Metastases. Cancers
(Basel). 16:9412024. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Ciracì P, Studiale V, Taravella A,
Antoniotti C and Cremolini C: Late-line options for patients with
metastatic colorectal cancer: A review and evidence-based
algorithm. Nat Rev Clin Oncol. 22:28–45. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Zheng W, Mu R, Liu F, Qin X, Li X, Yang P,
Li X, Liang Y and Zhu X: Textural features of the frontal white
matter could be used to discriminate amnestic mild cognitive
impairment patients from the normal population. Brain Behav.
13:e32222023. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Zhou Y, Wu D, Yan S, Xie Y, Zhang S, Lv W,
Qin Y, Liu Y, Liu C, Lu J, et al: Feasibility of a
Clinical-radiomics model to predict the outcomes of acute ischemic
stroke. Korean J Radiol. 23:811–820. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Grossmann P, Stringfield O, El-Hachem N,
Bui MM, Rios Velazquez E, Parmar C, Leijenaar RT, Haibe-Kains B,
Lambin P, Gillies RJ and Aerts HJ: Defining the biological basis of
radiomic phenotypes in lung cancer. Elife. 6:e234212017. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
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
|
|
30
|
Zhang H, Liao M, Guo Q, Chen J, Wang S,
Liu S and Xiao F: Predicting N2 lymph node metastasis in
presurgical stage I–II non-small cell lung cancer using multiview
radiomics and deep learning method. Med Phys. 50:2049–2060. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Li Y, Gong J, Shen X, Li M, Zhang H, Feng
F and Tong T: Assessment of primary colorectal Cancer CT radiomics
to predict metachronous liver metastasis. Front Oncol.
12:8618922022. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Hao M, Li H, Wang K, Liu Y, Liang X and
Ding L: Predicting metachronous liver metastasis in patients with
colorectal cancer: Development and assessment of a new nomogram.
World J Surg Oncol. 20:802022. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Ma YQ, Wen Y, Liang H, Zhong JG and Pang
PP: Magnetic resonance imaging-radiomics evaluation of response to
chemotherapy for synchronous liver metastasis of colorectal cancer.
World J Gastroenterol. 27:6465–6475. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Radiya K, Joakimsen HL, Mikalsen K, Aahlin
EK, Lindsetmo RO and Mortensen KE: Performance and clinical
applicability of machine learning in liver computed tomography
imaging: A systematic review. Eur Radiol. 33:6689–6717. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Liang M, Cai Z, Zhang H, Huang C, Meng Y,
Zhao L, Li D, Ma X and Zhao X: Machine Learning-based analysis of
rectal cancer MRI Radiomics for prediction of metachronous liver
metastasis. Acad Radiol. 26:1495–1504. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Li ZF, Kang LQ, Liu FH, Zhao M, Guo SY, Lu
S and Quan S: Radiomics based on preoperative rectal cancer MRI to
predict the metachronous liver metastasis. Abdom Radiol (NY).
48:833–843. 2023.PubMed/NCBI
|
