|
1
|
Siegel RL, Miller KD, Wagle NS and Jemal
A: Cancer statistics, 2023. CA Cancer J Clin. 73:17–48.
2023.PubMed/NCBI
|
|
2
|
Kato K, Machida R, Ito Y, Daiko H, Ozawa
S, Ogata T, Hara H, Kojima T, Abe T, Bamba T, et al: Doublet
chemotherapy, triplet chemotherapy, or doublet chemotherapy
combined with radiotherapy as neoadjuvant treatment for locally
advanced oesophageal cancer (JCOG1109 NExT): A randomised,
controlled, open-label, phase 3 trial. Lancet. 404:55–66. 2024.
View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Murakami K, Yoshida N, Taniyama Y,
Takahashi K, Toyozumi T, Uno T, Kamei T, Baba H and Matsubara H:
Maximum standardized uptake value change rate before and after
neoadjuvant chemotherapy can predict early recurrence in patients
with locally advanced esophageal cancer: A multi-institutional
cohort study of 220 patients in Japan. Esophagus. 19:205–213. 2022.
View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Swisher SG, Maish M, Erasmus JJ, Correa
AM, Ajani JA, Bresalier R, Komaki R, Macapinlac H, Munden RF,
Putnam JB, et al: Utility of PET, CT, and EUS to identify
pathologic responders in esophageal cancer. Ann Thorac Surg.
78:1152–1160. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Bergonie J and Tribondeau L:
Interpretation of some results of radiotherapy and an attempt at
determining a logical technique of treatment. Radiat Res.
11:587–588. 1959. View
Article : Google Scholar : PubMed/NCBI
|
|
6
|
Warburg O, Wind F and Negelein E: The
metabolism of tumors in the body. J Gen Physiol. 8:519–530. 1927.
View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Vaupel P and Multhoff G: The warburg
effect: Historical dogma versus current rationale. Adv Exp Med
Biol. 1269:169–177. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Dimitrakopoulou-Strauss A, Hohenberger P,
Pan L, Kasper B, Roumia S and Strauss LG: Dynamic PET with FDG in
patients with unresectable aggressive fibromatosis:
Regression-based parametric images and correlation to the FDG
kinetics based on a 2-tissue compartment model. Clin Nucl Med.
37:943–948. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Carmeliet P and Jain RK: Angiogenesis in
cancer and other diseases. Nature. 407:249–257. 2000. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Gatenby RA and Gillies RJ: Why do cancers
have high aerobic glycolysis? Nat Rev Cancer. 4:891–899. 2004.
View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Eisenhauer EA, Therasse P, Bogaerts J,
Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S,
Mooney M, et al: New response evaluation criteria in solid tumours:
revised RECIST guideline (version 1.1). Eur J Cancer. 45:228–247.
2009. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Hu J, Panin V, Smith AM, Spottiswoode B,
Shah V and von Gall CA: Design and implementation of automated
clinical whole body parametric PET with continuous bed motion. IEEE
Trans Radiat Plasma Med Sci. 4:696–707. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Watanabe M, Kato H, Katayama D, Soeda F,
Matsunaga K, Watabe T, Tatsumi M, Shimosegawa E and Tomiyama N:
Semiquantitative analysis using whole-body dynamic F-18
fluoro-2-deoxy-glucose-positron emission tomography to
differentiate between benign and malignant lesions. Ann Nucl Med.
36:951–963. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Patlak CS and Blasberg RG: Graphical
evaluation of blood-to-brain transfer constants from multiple-time
uptake data. Generalizations. J Cereb Blood Flow Metab. 5:584–590.
1985. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Osborne DR and Acuff S: Whole-body dynamic
imaging with continuous bed motion PET/CT. Nucl Med Commun.
37:428–431. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Phelps ME, Huang SC, Hoffman EJ, Selin C,
Sokoloff L and Kuhl DE: Tomographic measurement of local cerebral
glucose metabolic rate in humans with
(F-18)2-fluoro-2-deoxy-D-glucose: Validation of method. Ann Neurol.
6:371–388. 1979. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Lemarignier C, Di Fiore F, Marre C, Hapdey
S, Modzelewski R, Gouel P, Michel P, Dubray B and Vera P:
Pretreatment metabolic tumour volume is predictive of disease-free
survival and overall survival in patients with oesophageal squamous
cell carcinoma. Eur J Nucl Med Mol Imaging. 41:2008–2016. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Yamasaki M, Miyata H, Tanaka K, Shiraishi
O, Motoori M, Peng YF, Yasuda T, Yano M, Shiozaki H, Mori M and
Doki Y: Multicenter phase I/II study of docetaxel, cisplatin and
fluorouracil combination chemotherapy in patients with advanced or
recurrent squamous cell carcinoma of the esophagus. Oncology.
80:307–313. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Umeki Y, Matsuoka H, Fujita M, Goto A,
Serizawa A, Nakamura K, Akimoto S, Nakauchi M, Tanaka T, Shibasaki
S, et al: Docetaxel cisplatin 5-FU(DCF) therapy as a preoperative
chemotherapy to advanced esophageal squamous cell carcinoma: A
single-center retrospective cohort study. Internal Medicine.
62:319–325. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Katada C, Sugawara M, Hara H, Fujii H,
Nakajima TE, Ando T, Kojima T, Watanabe A, Sakamoto Y, Ishikawa H,
et al: A management of neutropenia using granulocyte colony
stimulating factor support for chemotherapy consisted of docetaxel,
cisplatin and 5-fluorouracil in patients with oesophageal squamous
cell carcinoma. Jpn J Clin Oncol. 51:199–204. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Hayano K, Okazumi S, Shuto K, Matsubara H,
Shimada H, Nabeya Y, Kazama T, Yanagawa N and Ochiai T: Perfusion
CT can predict the response to chemoradiation therapy and survival
in esophageal squamous cell carcinoma: Initial clinical results.
Oncol Rep. 18:901–908. 2007.PubMed/NCBI
|
|
22
|
Makari Y, Yasuda T, Doki Y, Miyata H,
Fujiwara Y, Takiguchi S, Matsuyama J, Yamasaki M, Hirao T, Koyama
MK, et al: Correlation between tumor blood flow assessed by
perfusion CT and effect of neoadjuvant therapy in advanced
esophageal cancers. J Surg Oncol. 96:220–229. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Wongwaiyut K, Ruangsin S, Laohawiriyakamol
S, Leelakiatpaiboon S, Sangthawan D, Sunpaweravong P and
Sunpaweravong S: Pretreatment esophageal wall thickness associated
with response to chemoradiotherapy in locally advanced esophageal
cancer. J Gastrointest Cancer. 51:947–951. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Kasai A, Miyoshi J, Sato Y, Okamoto K,
Miyamoto H, Kawanaka T, Tonoiso C, Harada M, Goto M, Yoshida T, et
al: A novel CT-based radiomics model for predicting response and
prognosis of chemoradiotherapy in esophageal squamous cell
carcinoma. Sci Rep. 14:20392024. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Aoyagi T, Shuto K, Okazumi S, Shimada H,
Kazama T and Matsubara H: Apparent diffusion coefficient values
measured by diffusion-weighted imaging predict
chemoradiotherapeutic effect for advanced esophageal cancer. Dig
Surg. 28:252–257. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
De Cobelli F, Giganti F, Orsenigo E,
Cellina M, Esposito A, Agostini G, Albarello L, Mazza E, Ambrosi A,
Socci C, et al: Apparent diffusion coefficient modifications in
assessing gastro-oesophageal cancer response to neoadjuvant
treatment: Comparison with tumour regression grade at histology.
Eur Radiol. 23:2165–2174. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Sun NN, Liu C, Ge XL and Wang J: Dynamic
contrast-enhanced MRI for advanced esophageal cancer response
assessment after concurrent chemoradiotherapy. Diagn Interv Radiol.
24:195–202. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Lei J, Han Q, Zhu S, Shi D, Dou S, Su Z
and Xu X: Assessment of esophageal carcinoma undergoing concurrent
chemoradiotherapy with quantitative dynamic contrast-enhanced
magnetic resonance imaging. Oncol Lett. 10:3607–3612. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Gu L, Xie X, Guo Z, Shen W, Qian P, Jiang
N and Fan Y: Dynamic contrast-enhanced magnetic resonance imaging:
A novel approach to assessing treatment in locally advanced
esophageal cancer patients. Niger J Clin Pract. 24:1800–1807. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Hirata A, Hayano K, Ohira G, Imanishi S,
Hanaoka T, Murakami K, Aoyagi T, Shuto K and Matsubara H:
Volumetric histogram analysis of apparent diffusion coefficient for
predicting pathological complete response and survival in
esophageal cancer patients treated with chemoradiotherapy. Am J
Surg. 219:1024–1029. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
van der Aa DC, Gisbertz SS, Anderegg MCJ,
Lagarde SM, Klaassen R, Meijer SL, van Dieren S, Hulshof M, Bergman
J, Bennink RJ, et al: (18)F-FDG-PET/CT to detect pathological
complete response after neoadjuvant treatment in patients with
cancer of the esophagus or gastroesophageal junction: Accuracy and
long-term implications. J Gastrointest Cancer. 55:270–280. 2024.
View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Weissinger M, Atmanspacher M, Spengler W,
Seith F, Von Beschwitz S, Dittmann H, Zender L, Smith AM, Casey ME,
Nikolaou K, et al: Diagnostic performance of dynamic whole-body
patlak [(18)F]FDG-PET/CT in patients with indeterminate lung
lesions and lymph nodes. J Clin Med. 12:39422023. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Wang Q, Wang RF, Zhang J and Zhou Y:
Differential diagnosis of pulmonary lesions by parametric imaging
in (18)F-FDG PET/CT dynamic multi-bed scanning. J BUON. 18:928–934.
2013.PubMed/NCBI
|
|
34
|
Parodi D, Dighero E, Biddau G, D'Amico F,
Bauckneht M, Marini C, Garbarino S, Campi C, Piana M and Sambuceti
G: Localized FDG loss in lung cancer lesions. EJNMMI Res.
14:1022024. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Skawran S, Messerli M, Kotasidis F,
Trinckauf J, Weyermann C, Kudura K, Ferraro DA, Pitteloud J, Treyer
V, Maurer A, et al: Can dynamic whole-body FDG PET imaging
differentiate between malignant and inflammatory lesions? Life
(Basel). 12:13502022.PubMed/NCBI
|
|
36
|
Huang X, Zhuang M, Yang S, Wang Y, Liu Q,
Xu X, Xiao M, Peng Y, Jiang P, Xu W, et al: The valuable role of
dynamic (18)F FDG PET/CT-derived kinetic parameter K(i) in patients
with nasopharyngeal carcinoma prior to radiotherapy: A prospective
study. Radiother Oncol. 179:1094402023. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Sundaraiya S, T R, Nangia S, Sirohi B and
Patil S: Role of dynamic and parametric whole-body FDG PET/CT
imaging in molecular characterization of primary breast cancer: A
single institution experience. Nucl Med Commun. 43:1015–1025. 2022.
View Article : Google Scholar : PubMed/NCBI
|
|
38
|
van Berkel A, Vriens D, Visser EP, Janssen
MJR, Gotthardt M, Hermus ARMM, Geus-Oei LF and Timmers HJLM:
Metabolic Subtyping of Pheochromocytoma and Paraganglioma by
(18)F-FDG Pharmacokinetics Using Dynamic PET/CT Scanning. J Nucl
Med. 60:745–751. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Kaneko K, Nagao M, Yamamoto A, Yano K,
Honda G, Tokushige K and Sakai S: Patlak reconstruction using
dynamic 18 F-FDG PET imaging for evaluation of malignant liver
tumors: A comparison of HCC, ICC, and metastatic liver tumors. Clin
Nucl Med. 49:116–123. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Lv W, Yang M, Zhong H, Wang X, Yang S, Bi
L, Xian J, Pei X, He X, Wang Y, et al: Application of dynamic
(18)F-FDG PET/CT for distinguishing intrapulmonary metastases from
synchronous multiple primary lung cancer. Mol Imaging.
2022:80812992022. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Wang D, Qiu B, Liu Q, Xia L, Liu S, Zheng
C, Liu H, Mo Y, Zhang X, Hu Y, et al: Patlak-Ki derived from
ultra-high sensitivity dynamic total body [(18)F]FDG PET/CT
correlates with the response to induction immuno-chemotherapy in
locally advanced non-small cell lung cancer patients. Eur J Nucl
Med Mol Imaging. 50:3400–3413. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
de Geus-Oei LF, van Laarhoven HW, Visser
EP, Hermsen R, van Hoorn BA, Kamm YJ, Krabbe PF, Corstens FH, Punt
CJ and Oyen WJ: Chemotherapy response evaluation with FDG-PET in
patients with colorectal cancer. Ann Oncol. 19:348–352. 2008.
View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Dimitrakopoulou-Strauss A, Strauss LG,
Egerer G, Vasamiliette J, Mechtersheimer G, Schmitt T, Lehner B,
Haberkorn U, Stroebel P and Kasper B: Impact of dynamic 18F-FDG PET
on the early prediction of therapy outcome in patients with
high-risk soft-tissue sarcomas after neoadjuvant chemotherapy: A
feasibility study. J Nucl Med. 51:551–558. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Hofheinz F, Hoff JV, Steffen IG, Lougovski
A, Ego K, Amthauer H and Apostolova I: Comparative evaluation of
SUV, tumor-to-blood standard uptake ratio (SUR), and dual time
point measurements for assessment of the metabolic uptake rate in
FDG PET. EJNMMI Res. 6:532016. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Dimitrakopoulou-Strauss A, Hoffmann M,
Bergner R, Uppenkamp M, Eisenhut M, Pan L, Haberkorn U and Strauss
LG: Prediction of short-term survival in patients with advanced
nonsmall cell lung cancer following chemotherapy based on
2-deoxy-2-[F-18]fluoro-D-glucose-positron emission tomography: A
feasibility study. Mol Imaging Biol. 9:308–317. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Okazumi S, Ohira G, Hayano K, Aoyagi T,
Imanishi S and Matsubara H: Novel advances in qualitative
diagnostic imaging for decision making in multidisciplinary
treatment for advanced esophageal cancer. J Clin Med. 13:6322024.
View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Hayano K, Ohira G, Hirata A, Aoyagi T,
Imanishi S, Tochigi T, Hanaoka T, Shuto K and Matsubara H: Imaging
biomarkers for the treatment of esophageal cancer. World J
Gastroenterol. 25:3021–3029. 2019. View Article : Google Scholar : PubMed/NCBI
|
