1. 厚生労働省ウェブサイト．がん登録
全国がん登録
罹患数・率
報告
平成
28 年報告；2019 年 https://www.mhlw.go.jp/content/10900000/000624853.pdf
2. Engstrand, J., H. Nilsson, C. Strömberg, E. Jonas & J. Freedman. Colorectal
cancer liver metastases - a population-based study on incidence, management
and survival. BMC Cancer, 18, 78, 2018.
3. 大腸癌研究会編
大腸癌取扱い規約第 9 版, p113, 2018
4. 大 腸 癌 研 究 会 編
大 腸 癌 治 療 ガ イ ド ラ イ ン
2019
年 版
http://www.jsccr.jp/guideline/2019/particular.html#no2
5. Choti, M. A., J. V. Sitzmann, M. F. Tiburi, W. Sumetchotimetha, R. Rangsin, R.
D. Schulick, K. D. Lillemoe, C. J. Yeo & J. L. Cameron. Trends in long-term
survival following liver resection for hepatic colorectal metastases. Ann Surg, 235,
759-66, 2002.
6. Ito, K., A. Govindarajan, H. Ito & Y. Fong. Surgical treatment of hepatic
colorectal metastasis: evolving role in the setting of improving systemic therapies
57
and ablative treatments in the 21st century. Cancer J, 16, 103-10, 2010.
7. Scheele, J., R. Stang, A. Altendorf-Hofmann & M. Paul. Resection of colorectal
liver metastases. World J Surg, 19, 59-71, 1995.
8. Nordlinger, B., M. Guiguet, J. C. Vaillant, P. Balladur, K. Boudjema, P. Bachellier
& D. Jaeck. Surgical resection of colorectal carcinoma metastases to the liver. A
prognostic scoring system to improve case selection, based on 1568 patients.
Association Française de Chirurgie. Cancer, 77, 1254-62, 1996.
9. Adam, R., E. Avisar, A. Ariche, S. Giachetti, D. Azoulay, D. Castaing, F.
Kunstlinger, F. Levi & F. Bismuth. Five-year survival following hepatic resection
after neoadjuvant therapy for nonresectable colorectal. Ann Surg Oncol, 8, 34753, 2001.
10. Kato, T., K. Yasui, T. Hirai, Y. Kanemitsu, T. Mori, K. Sugihara, H. Mochizuki &
J. Yamamoto. Therapeutic results for hepatic metastasis of colorectal cancer with
special reference to effectiveness of hepatectomy: analysis of prognostic factors
for 763 cases recorded at 18 institutions. Dis Colon Rectum, 46, S22-31, 2003a.
11. Jo, H. S., D. S. Kim, S. W. Jung, Y. D. Yu, S. B. Choi, W. B. Kim, H. J. Han & T.
J. Song. Clinical significance of post-hepatectomy hepatic failure in patients with
liver metastases from colorectal cancer. Ann Hepatobiliary Pancreat Surg, 22, 93100, 2018.
58
12. Akgül, Ö., E. Çetinkaya, Ş. Ersöz & M. Tez. Role of surgery in colorectal cancer
liver metastases. World J Gastroenterol, 20, 6113-22, 2014.
13. Kato, T., K. Yasui, T. Hirai, Y. Kanemitsu, T. Mori, K. Sugihara, H. Mochizuki &
J. Yamamoto. Therapeutic results for hepatic metastasis of colorectal cancer with
special reference to effectiveness of hepatectomy: analysis of prognostic factors
for 763 cases recorded at 18 institutions. Dis Colon Rectum, 46, S22-31, 2003.
14. Figueras, J., J. Torras, C. Valls, L. Llado, E. Ramos, J. Marti-Ragué, T. Serrano &
J. Fabregat. Surgical resection of colorectal liver metastases in patients with
expanded indications: a single-center experience with 501 patients. Dis Colon
Rectum, 50, 478-88, 2007.
15. Wei, A. C., P. D. Greig, D. Grant, B. Taylor, B. Langer & S. Gallinger. Survival
after hepatic resection for colorectal metastases: a 10-year experience. Ann Surg
Oncol, 13, 668-76, 2006.
16. Rees, M., P. P. Tekkis, F. K. Welsh, T. O'Rourke & T. G. John. Evaluation of
long-term survival after hepatic resection for metastatic colorectal cancer: a
multifactorial model of 929 patients. Ann Surg, 247, 125-35, 2008.
17. Fong, Y., J. Fortner, R. L. Sun, M. F. Brennan & L. H. Blumgart. Clinical score
for predicting recurrence after hepatic resection for metastatic colorectal cancer:
analysis of 1001 consecutive cases. Ann Surg, 230, 309-18; discussion 318-21,
59
1999.
18. Dexiang, Z., R. Li, W. Ye, W. Haifu, Z. Yunshi, Y. Qinghai, Z. Shenyong, X. Bo,
L. Li, P. Xiangou, L. Haohao, Y. Lechi, L. Tianshu, F. Jia, Q. Xinyu & X. Jianmin.
Outcome of patients with colorectal liver metastasis: analysis of 1,613
consecutive cases. Ann Surg Oncol, 19, 2860-8, 2012.
19. Chen, Y., W. Chang, L. Ren, J. Chen, W. Tang, T. Liu, M. Jian, Y. Liu, Y. Wei &
J. Xu. Comprehensive Evaluation of Relapse Risk (CERR) Score for Colorectal
Liver Metastases: Development and Validation. Oncologist, 25, e1031-e1041,
2020.
20. Lunevicius, R., H. Nakanishi, S. Ito, K. Kozaki, T. Kato, M. Tatematsu & K. Yasui.
Clinicopathological significance of fibrotic capsule formation around liver
metastasis from colorectal cancer. J Cancer Res Clin Oncol, 127, 193-9, 2001.
21. Okano, K., J. Yamamoto, T. Kosuge, S. Yamamoto, M. Sakamoto, Y. Nakanishi
& S. Hirohashi. Fibrous pseudocapsule of metastatic liver tumors from colorectal
carcinoma. Clinicopathologic study of 152 first resection cases. Cancer, 89, 26775, 2000.
22. Wiggans, M. G., G. Shahtahmassebi, P. Malcolm, F. McCormick, S. Aroori, M. J.
Bowles & D. A. Stell. Extended pathology reporting of resection specimens of
colorectal liver metastases: the significance of a tumour pseudocapsule. HPB
60
(Oxford), 15, 687-94, 2013.
23. Yamamoto, J., K. Shimada, T. Kosuge, S. Yamasaki, M. Sakamoto & H. Fukuda.
Factors influencing survival of patients undergoing hepatectomy for colorectal
metastases. Br J Surg, 86, 332-7, 1999.
24. Hyngstrom, J. R., C. Y. Hu, Y. Xing, Y. N. You, B. W. Feig, J. M. Skibber, M. A.
Rodriguez-Bigas, J. N. Cormier & G. J. Chang. Clinicopathology and outcomes
for mucinous and signet ring colorectal adenocarcinoma: analysis from the
National Cancer Data Base. Ann Surg Oncol, 19, 2814-21, 2012.
25. Lupinacci, R. M., E. S. Mello, F. F. Coelho, J. A. Kruger, M. V. Perini, R. S.
Pinheiro, G. M. Fonseca, I. Cecconello & P. Herman. Prognostic implication of
mucinous histology in resected colorectal cancer liver metastases. Surgery, 155,
1062-8, 2014.
26. Knijn, N., J. A. de Ridder, C. J. Punt, J. H. de Wilt & I. D. Nagtegaal.
Histopathological evaluation of resected colorectal cancer liver metastases: what
should be done? Histopathology, 63, 149-56, 2013.
27. Reijonen, P., P. Österlund, H. Isoniemi, J. Arola & A. Nordin. Histologically
Verified Biliary Invasion was Associated with Impaired Liver Recurrence-Free
Survival in Resected Colorectal Cancer Liver Metastases. Scand J Surg, 108, 201209, 2019.
61
28. Gillies, R. J., P. E. Kinahan & H. Hricak. Radiomics: Images Are More than
Pictures, They Are Data. Radiology, 278, 563-77, 2016.
29. Hanahan, D. & R. A. Weinberg. Hallmarks of cancer: the next generation. Cell,
144, 646-74, 2011.
30. Gerlinger, M., A. J. Rowan, S. Horswell, M. Math, J. Larkin, D. Endesfelder, E.
Gronroos, P. Martinez, N. Matthews, A. Stewart, P. Tarpey, I. Varela, B.
Phillimore, S. Begum, N. Q. McDonald, A. Butler, D. Jones, K. Raine, C. Latimer,
C. R. Santos, M. Nohadani, A. C. Eklund, B. Spencer-Dene, G. Clark, L.
Pickering, G. Stamp, M. Gore, Z. Szallasi, J. Downward, P. A. Futreal & C.
Swanton. Intratumor heterogeneity and branched evolution revealed by
multiregion sequencing. N Engl J Med, 366, 883-892, 2012.
31. Jamal-Hanjani, M., G. A. Wilson, N. McGranahan, N. J. Birkbak, T. B. K.
Watkins, S. Veeriah, S. Shafi, D. H. Johnson, R. Mitter, R. Rosenthal, M. Salm, S.
Horswell, M. Escudero, N. Matthews, A. Rowan, T. Chambers, D. A. Moore, S.
Turajlic, H. Xu, S. M. Lee, M. D. Forster, T. Ahmad, C. T. Hiley, C. Abbosh, M.
Falzon, E. Borg, T. Marafioti, D. Lawrence, M. Hayward, S. Kolvekar, N.
Panagiotopoulos, S. M. Janes, R. Thakrar, A. Ahmed, F. Blackhall, Y. Summers,
R. Shah, L. Joseph, A. M. Quinn, P. A. Crosbie, B. Naidu, G. Middleton, G.
Langman, S. Trotter, M. Nicolson, H. Remmen, K. Kerr, M. Chetty, L. Gomersall,
62
D. A. Fennell, A. Nakas, S. Rathinam, G. Anand, S. Khan, P. Russell, V. Ezhil, B.
Ismail, M. Irvin-Sellers, V. Prakash, J. F. Lester, M. Kornaszewska, R. Attanoos,
H. Adams, H. Davies, S. Dentro, P. Taniere, B. O'Sullivan, H. L. Lowe, J. A.
Hartley, N. Iles, H. Bell, Y. Ngai, J. A. Shaw, J. Herrero, Z. Szallasi, R. F. Schwarz,
A. Stewart, S. A. Quezada, J. Le Quesne, P. Van Loo, C. Dive, A. Hackshaw, C.
Swanton & T. Consortium. Tracking the Evolution of Non-Small-Cell Lung
Cancer. N Engl J Med, 376, 2109-2121, 2017.
32. Diehn, M.et al. Identification of noninvasive imaging surrogates for brain tumor
gene-expression modules. Proc Natl Acad Sci U S A, 105, 5213-8, 2008.
33. Gutman, D. A., W. D. Dunn, P. Grossmann, L. A. Cooper, C. A. Holder, K. L.
Ligon, B. M. Alexander & H. J. Aerts. Somatic mutations associated with MRIderived volumetric features in glioblastoma. Neuroradiology, 57, 1227-37, 2015.
34. Aerts, H. J., E. R. Velazquez, R. T. Leijenaar, C. Parmar, P. Grossmann, S.
Carvalho, S. Cavalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld,
F. Hoebers, M. M. Rietbergen, C. R. Leemans, A. Dekker, J. Quackenbush, R. J.
Gillies & P. Lambin. Decoding tumour phenotype by noninvasive imaging using
a quantitative radiomics approach. Nat Commun, 5, 4006, 2014.
35. Lorensen WE, Cline HE. Marching cubes: A high resolution 3D surface
construction algorithm. ACM SIGGRAPH Comput Graph Internet. 21,163-9,
63
1987.
36. CH Chen, F Pau, PSP Wang. Handbook of Pattern Recognition and Computer
Vision, 1998
37. Davnall, F., C. S. Yip, G. Ljungqvist, M. Selmi, F. Ng, B. Sanghera, B. Ganeshan,
K. A. Miles, G. J. Cook & V. Goh. Assessment of tumor heterogeneity: an
emerging imaging tool for clinical practice? Insights Imaging, 3, 573-89, 2012.
38. Lubner, M. G., A. D. Smith, K. Sandrasegaran, D. V. Sahani & P. J. Pickhardt.
CT Texture Analysis: Definitions, Applications, Biologic Correlates, and
Challenges. Radiographics, 37, 1483-1503, 2017.
39. Haralick RM, Shanmugam K, Dinstein I. Textural features for image
classification. IEEE Trans Syst Man Cybern ;SMC-3(6):610–621, 1973.
40. Harrell FE Jr. Regression modeling strategies with applications to linear models,
logistic and ordinal regression, and survival analysis. New York, NY: SpringerVerlag, 2015.
41. R. Tibshirani, “Regression shrinkage and selection via the LASSO,” Journal of
the Royal Statistical Society : Series B (Statistical Methodology), vol. 58, no. 1,
pp. 267-288, 1996.
42. Parmar, C., P. Grossmann, J. Bussink, P. Lambin & H. J. W. L. Aerts. Machine
Learning methods for Quantitative Radiomic Biomarkers. Sci Rep, 5, 13087,
64
2015.
43. Moons, K. G., A. P. Kengne, M. Woodward, P. Royston, Y. Vergouwe, D. G.
Altman & D. E. Grobbee. Risk prediction models: I. Development, internal
validation, and assessing the incremental value of a new (bio)marker. Heart, 98,
683-90, 2012.
44. Huang, Y., Z. Liu, L. He, X. Chen, D. Pan, Z. Ma, C. Liang & J. Tian. Radiomics
Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in
Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology, 281, 947-957, 2016.
45. Oyama, A., Y. Hiraoka, I. Obayashi, Y. Saikawa, S. Furui, K. Shiraishi, S. Kumagai,
T. Hayashi & J. Kotoku. Hepatic tumor classification using texture and topology
analysis of non-contrast-enhanced three-dimensional T1-weighted MR images
with a radiomics approach. Sci Rep, 9, 8764, 2019.
46. Dercle, L., L. Lu, L. H. Schwartz, M. Qian, S. Tejpar, P. Eggleton, B. Zhao & H.
Piessevaux. Radiomics Response Signature for Identification of Metastatic
Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway. J Natl
Cancer Inst, 112, 902-912, 2020.
47. Lubner, M. G., N. Stabo, S. J. Lubner, A. M. del Rio, C. Song, R. B. Halberg & P.
J. Pickhardt. CT textural analysis of hepatic metastatic colorectal cancer: pretreatment tumor heterogeneity correlates with pathology and clinical outcomes.
65
Abdom Imaging, 40, 2331-7, 2015.
48. Simpson, A. L., A. Doussot, J. M. Creasy, L. B. Adams, P. J. Allen, R. P. DeMatteo,
M. Gönen, N. E. Kemeny, T. P. Kingham, J. Shia, W. R. Jarnagin, R. K. G. Do &
M. I. D'Angelica. Computed Tomography Image Texture: A Noninvasive
Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic
Colorectal Cancer. Ann Surg Oncol, 24, 2482-2490, 2017.
49. Andersen, I. R., K. Thorup, M. B. Andersen, R. Olesen, F. V. Mortensen, D. T.
Nielsen & F. Rasmussen. Texture in the monitoring of regorafenib therapy in
patients with colorectal liver metastases. Acta Radiol, 60, 1084-1093, 2019.
50. Beckers, R. C. J., S. Trebeschi, M. Maas, R. S. Schnerr, J. M. L. Sijmons, G. L.
Beets, J. B. Houwers, R. G. H. Beets-Tan & D. M. J. Lambregts. CT texture
analysis in colorectal liver metastases and the surrounding liver parenchyma and
its potential as an imaging biomarker of disease aggressiveness, response and
survival. Eur J Radiol, 102, 15-21, 2018.
51. Dohan, A., B. Gallix, B. Guiu, K. Le Malicot, C. Reinhold, P. Soyer, J. Bennouna,
F. Ghiringhelli, E. Barbier, V. Boige, J. Taieb, O. Bouché, E. François, J. M.
Phelip, C. Borel, R. Faroux, J. F. Seitz, S. Jacquot, M. Ben Abdelghani, F.
Khemissa-Akouz, D. Genet, J. L. Jouve, Y. Rinaldi, F. Desseigne, P. Texereau, E.
Suc, C. Lepage, T. Aparicio, C. Hoeffel & P. I. a. P. Investigators. Early evaluation
66
using a radiomic signature of unresectable hepatic metastases to predict outcome
in patients with colorectal cancer treated with FOLFIRI and bevacizumab. Gut,
69, 531-539, 2020.
52. Rahmim, A., K. P. Bak-Fredslund, S. Ashrafinia, L. Lu, C. R. Schmidtlein, R. M.
Subramaniam, A. Morsing, S. Keiding, J. Horsager & O. L. Munk. Prognostic
modeling for patients with colorectal liver metastases incorporating FDG PET
radiomic features. Eur J Radiol, 113, 101-109, 2019.
53. Ravanelli, M., G. M. Agazzi, E. Tononcelli, E. Roca, P. Cabassa, G. Baiocchi, A.
Berruti, R. Maroldi & D. Farina. Texture features of colorectal liver metastases
on pretreatment contrast-enhanced CT may predict response and prognosis in
patients treated with bevacizumab-containing chemotherapy: a pilot study
including comparison with standard chemotherapy. Radiol Med, 124, 877-886,
2019.
54. Shur, J., M. Orton, A. Connor, S. Fischer, C. A. Moulton, S. Gallinger, D. M. Koh
& K. S. Jhaveri. A clinical-radiomic model for improved prognostication of
surgical candidates with colorectal liver metastases. J Surg Oncol, 2019.
55. Cheng, J., J. Wei, T. Tong, W. Sheng, Y. Zhang, Y. Han, D. Gu, N. Hong, Y. Ye,
J. Tian & Y. Wang. Prediction of Histopathologic Growth Patterns of Colorectal
Liver Metastases with a Noninvasive Imaging Method. Ann Surg Oncol, 26,
67
4587-4598, 2019.
56. van Helden, E. J., Y. J. L. Vacher, W. N. van Wieringen, F. H. P. van Velden, H.
M. W. Verheul, O. S. Hoekstra, R. Boellaard & C. W. Menke-van der Houven van
Oordt. Radiomics analysis of pre-treatment [Eur J Nucl Med Mol Imaging, 45,
2307-2317, 2018.
57. Zhang, H., W. Li, F. Hu, Y. Sun, T. Hu & T. Tong. MR texture analysis: potential
imaging biomarker for predicting the chemotherapeutic response of patients with
colorectal liver metastases. Abdom Radiol (NY), 44, 65-71, 2019.
58. Caruso, D., M. Zerunian, M. Ciolina, D. de Santis, M. Rengo, M. H. Soomro, G.
Giunta, S. Conforto, M. Schmid, E. Neri & A. Laghi. Haralick's texture features
for the prediction of response to therapy in colorectal cancer: a preliminary study.
Radiol Med, 123, 161-167, 2018.
59. Rubbia-Brandt, L., E. Giostra, C. Brezault, A. D. Roth, A. Andres, V. Audard, P.
Sartoretti, B. Dousset, P. E. Majno, O. Soubrane, S. Chaussade, G. Mentha & B.
Terris. Importance of histological tumor response assessment in predicting the
outcome in patients with colorectal liver metastases treated with neo-adjuvant
chemotherapy followed by liver surgery. Ann Oncol, 18, 299-304, 2007.
60. Boonsirikamchai, P., M. A. Asran, D. M. Maru, J. N. Vauthey, H. Kaur, S. Kopetz
& E. M. Loyer. CT findings of response and recurrence, independent of change
68
in tumor size, in colorectal liver metastasis treated with bevacizumab. AJR Am J
Roentgenol, 197, W1060-6, 2011.
61. Liu, W., J. G. Zhou, Y. Sun, L. Zhang & B. C. Xing. The role of neoadjuvant
chemotherapy for resectable colorectal liver metastases: a systematic review and
meta-analysis. Oncotarget, 7, 37277-37287, 2016.
62. Nordlinger B, Sorbye H, Glimelius B, et al. Perioperative FOLFOX4
chemotherapy and surgery versus surgery alone for resectable liver metastases
from colorectal cancer (EORTC 40983): long-term results of a randomised,
controlled, phase 3 trial. Lancet Oncol, 14(12):1208–1215, 2013.
63. Antoniou, A., R. E. Lovegrove, H. S. Tilney, A. G. Heriot, T. G. John, M. Rees,
P. P. Tekkis & F. K. Welsh (2007) Meta-analysis of clinical outcome after first
and second liver resection for colorectal metastases. Surgery, 141, 9-18.
64. National Comprehensive Cancer Network. Colon Cancer (Version 4.2020).
https://www.nccn.org/professionals/physician_gls/pdf/colon.pdf. Accessed July
31, 2020.)
65. Park, S. H., H. Kim, E. K. Kim, D. K. Choi, Y. E. Chung, M. J. Kim & J. Y. Choi
Aberrant expression of OATP1B3 in colorectal cancer liver metastases and its
clinical implication on gadoxetic acid-enhanced MRI. Oncotarget, 8, 7101271023, 2017.
69
66. Cheung, H. M. C., P. J. Karanicolas, N. Coburn, V. Seth, C. Law & L. Milot
Delayed tumour enhancement on gadoxetate-enhanced MRI is associated with
overall survival in patients with colorectal liver metastases. Eur Radiol, 29, 10321038, 2019.
67. Outwater, E., J. E. Tomaszewski, J. M. Daly & H. Y. Kressel Hepatic colorectal
metastases: correlation of MR imaging and pathologic appearance. Radiology,
180, 327-32, 1991.
68. Yushkevich, P. A., J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee & G.
Gerig. User-guided 3D active contour segmentation of anatomical structures:
significantly improved efficiency and reliability. Neuroimage, 31, 1116-28, 2006.
69. van Griethuysen, J. J. M., A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V.
Narayan, R. G. H. Beets-Tan, J. C. Fillion-Robin, S. Pieper & H. J. W. L. Aerts
Computational Radiomics System to Decode the Radiographic Phenotype.
Cancer Res, 77, e104-e107, 2017.
70. Beare, R., B. Lowekamp & Z. Yaniv. Image Segmentation, Registration and
Characterization in R with SimpleITK. J Stat Softw, 86, 2018.
71. van Griethuysen, J. J. M., A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V.
Narayan, R. G. H. Beets-Tan, J. C. Fillion-Robin, S. Pieper & H. J. W. L. Aerts.
Computational Radiomics System to Decode the Radiographic Phenotype.
70
Cancer Res, 77, e104-e107, 2017.
72. Yaniv, Z., B. C. Lowekamp, H. J. Johnson & R. Beare. SimpleITK Image-Analysis
Notebooks: a Collaborative Environment for Education and Reproducible
Research. J Digit Imaging, 31, 290-303, 2018.
73. Fonseca GM, Herman P, Faraj SF, et al. Pathological factors and prognosis of
resectedliver metastases of colorectal carcinoma: implications and proposal for a
pathologicalreporting protocol. Histopathology, 72(3):377–390, 2018.
74. Harrell, F. E., R. M. Califf, D. B. Pryor, K. L. Lee & R. A. Rosati. Evaluating the
yield of medical tests. JAMA, 247, 2543-6, 1982.
75. Statistical
Odds
Ends,
https://statisticaloddsandends.wordpress.com/2019/10/26/what-is-harrells-cindex/ 最終閲覧日：2021/1/5
76. Friedman, J.H. On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality.
Data Mining and Knowledge Discovery 1, 55–77, 1997.
77. Semelka, R. C., S. M. Hussain, H. B. Marcos & J. T. Woosley. Perilesional
enhancement of hepatic metastases: correlation between MR imaging and
histopathologic findings-initial observations. Radiology, 215, 89-94, 2000.
78. Feng, S. T., Y. Jia, B. Liao, B. Huang, Q. Zhou, X. Li, K. Wei, L. Chen, B. Li, W.
Wang, S. Chen, X. He, H. Wang, S. Peng, Z. B. Chen, M. Tang, Z. Chen, Y. Hou,
71
Z. Peng & M. Kuang. Preoperative prediction of microvascular invasion in
hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI.
Eur Radiol, 29, 4648-4659, 2019.
79. Xu, X., H. L. Zhang, Q. P. Liu, S. W. Sun, J. Zhang, F. P. Zhu, G. Yang, X. Yan,
Y. D. Zhang & X. S. Liu. Radiomic analysis of contrast-enhanced CT predicts
microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol, 70,
1133-1144, 2019.
80. Peduzzi, P., J. Concato, E. Kemper, T. R. Holford & A. R. Feinstein. A simulation
study of the number of events per variable in logistic regression analysis. J Clin
Epidemiol, 49, 1373-9, 1996.
81. Kumar, V., Y. Gu, S. Basu, A. Berglund, S. A. Eschrich, M. B. Schabath, K. Forster,
H. J. Aerts, A. Dekker, D. Fenstermacher, D. B. Goldgof, L. O. Hall, P. Lambin,
Y. Balagurunathan, R. A. Gatenby & R. J. Gillies. Radiomics: the process and the
challenges. Magn Reson Imaging, 30, 1234-48, 2012.
82. Rios Velazquez, E., H. J. Aerts, Y. Gu, D. B. Goldgof, D. De Ruysscher, A. Dekker,
R. Korn, R. J. Gillies & P. Lambin. A semiautomatic CT-based ensemble
segmentation of lung tumors: comparison with oncologists' delineations and with
the surgical specimen. Radiother Oncol, 105, 167-73, 2012.
83. Trevor Hastie et al. The Elements of Statistical Learning 12th edition, p56, 2017.
72
84. Wu, W., C. Parmar, P. Grossmann, J. Quackenbush, P. Lambin, J. Bussink, R.
Mak & H. J. Aerts. Exploratory Study to Identify Radiomics Classifiers for Lung
Cancer Histology. Front Oncol, 6, 71, 2016.
9． 謝辞
本研究を施行するにあたりまして、御指導を賜りました東京大学大学院医学
系研究科生体物理医学専攻放射線診断学教授 阿部
修先生に厚く御礼申し上
げます。
研究計画の検討や、データの分析、論文の校閲など多岐にわたり、御指導、御尽力
いただきました東京大学医学部附属病院放射線診断学講師 花岡 昇平先生、助教
中井 雄大先生に厚く御礼申し上げます。
肝胆膵外科・人工臓器移植外科の長谷川
潔教授、有田
淳一先生、西岡
先生には貴重な症例データをお貸しいただき、病理部の牛久
裕次郎
哲男先生、阿部
幸先生には病理組織標本の再評価をしていただきました。厚く感謝を申し上げます。
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