Highlights
- •Both satellite lesions and other multiple lesions found to be prognostic factors
- •Tumor distribution and number should be reported in intrahepatic cholangiocarcinoma
- •Evaluation of both tumor distribution and number could improve risk stratification
Abstract
Background
Methods
Results
Conclusion
Keywords
Abbreviations
AJCC1. Introduction
- Burgart L.
- Chopp W.
- Jain D.
2. Materials and methods
- Jansson H.
- Sparrelid E.
2.1 Literature search

2.2 Data extraction
- Mitchell M.
- Muftakhidinov B.
- Winchen T.
2.3 Statistical analysis
3. Results
3.1 Identification of studies
Ke Q, Wang L, Lin Z, Lou J, Zheng S, Bi X, Wang J, Guo W, Li F, Wang J, Zheng Y, Li J, Cheng S, Zhou W, Zeng Y. Prognostic Value of Lymph Node Dissection for Intrahepatic Cholangiocarcinoma Patients With Clinically Negative Lymph Node Metastasis: A Multi-Center Study From China. Front Oncol. 2021 Mar 11;11:585808.
- Tsilimigras D.I.
- Mehta R.
- Moris D.
- Sahara K.
- Bagante F.
- Paredes A.Z.
- et al.
Study (year) | Time period/Country or region | No. of pts.&/single- or multi-centre | iCCA sub-group | % Mass-form. | % Women | Md age (Y)/Md max size (mm) | Diagn.mod. | % Mult./Satell. | % Other group | % Sub-group no. (no. of lesions) | % Cirrhosis/% HBV or HBV + HCV |
---|---|---|---|---|---|---|---|---|---|---|---|
Addeo (2019) [ [18] ] | 1995–2017 | 119 | 64 | 47 | 13c | ||||||
FR | single | 78# | 33a | ||||||||
Aherne (2018) [ [32] ] | 1993–2014 | 66 | MF! | 100 | 62 | 65 | CT | – | 6d | ||
US | single | 59 | 18 | ||||||||
Bartsch (2021) [ [19] ] | 2000–2018 | 125 | 49 | 64 | CT/MRI | 25 | 8c¶ | ||||
GER | single | 10 | |||||||||
Bednarsch (2021) [ [50] ] | 2010–2020 | 139 | 56 | 65# | CT/M | 29.5 | 12 (2–3) | ||||
GER | single | RI | 7 (4–5) | ||||||||
10 (>5) | |||||||||||
Brustia (2020) [ [51] ] | 2001–2018 | 246 | 48e | 68e | PAT† | – | 9$ | 9e | |||
EUR, BR, JPN | multi | 50e | 20 | 18e | |||||||
Buettner (2017) [ [52] ] | 1990–2016 | 1054 | 92 | 46 | 59 | – | 7d | ||||
N. AM, EUR, ASIA, AUS | multif | 61 | 23 | ||||||||
Chen Q (2020) [ [33] ] | 2006–2011 | 305 | CIR | 13 | 52 | 41 | 100 | ||||
CHI | singlef | 50 | 20 | 87 | |||||||
Chen Y (2021) [ [59] ] | 2009–2017 | 704 | R0 | 42 | PAT | 28 | – | ||||
CHI | multi | 27 | 36 | ||||||||
Conci (2018) [ [53] ] | 1995–2015 | 243 | 50 | 68 | PAT | 35 | 14$ | ||||
IT | multik | 57 | 22 | ||||||||
De Rose (2013) [ [54] ] | 2000–2009 | 79 | MF | 100 | 42 | 67 | CT/MRI | – | – | ||
IT | multij | 65 | 44b | 34 | |||||||
Fu (2020) [ [34] ] | 2006–2011 | 110 | 44 | 53# | 40 | 21d | |||||
CHI | single | – | |||||||||
Kim (2014) [ [35] ] | 2000–2009 | 77 | 35 | 61 | – | 3$ | – | ||||
KOR | single | 50 | – | 17d @ | 21 | ||||||
King (2020) [ [36] ] | 2003–2017 | 52s | MF | 100 | 64e | 63e | CT | – | 5$e | 4e | |
US | singlek | 68e | 27o | 15e | |||||||
Kudo (2021) [ [60] ] | 2002–2013 | 3022 | 71& | 39& ^ | PAT | 15 | 6 (2) | 8& | |||
JPN | multi | – | 9 (>2) | ||||||||
Lee (2014) [ [37] ] | 1995–2003 | 53 | 62e | 61#e | – | ||||||
US | single | 79#e | 32 | ||||||||
Liu (2018) [ [38] ] | 2005–2011 | 214 | 43 | 58 | 26 | 28 | |||||
CHI | singlei | 55 | – | 40 | |||||||
Lu (2016) [ [39] ] | 2000–2005 | 303 | MF | 100 | 32 | 52# | – | 32 | |||
CHI | singleg | 68# | – | 53 | |||||||
Moro (2019) [ [55] ] | 1990–2016 | 1145 | 84§ | 45 | 60 | – | 10 | ||||
N. AM, EUR, ASIA, AUS | multif | 60 | 20 | 17 | |||||||
Nuzzo (2010) [ [40] ] | 1997–2008 | 55 | 86 | 45 | 64 | 36 | 15 (2) | – | |||
IT | single | 60# | – | 22 (>2) | 38 | ||||||
Scheuermann (2013) [ [41] ] | 1997–2012 | 130 | 50 | 65 | PAT | – | 75 (1–3) | – | |||
GER | single | 70 | – | 25 (>3) | 3 | ||||||
Shen (2018) [ [42] ] | 2009–2013 | 91 | 49 | 57 | – | 8$ | 15 | ||||
CHI | singleh | 64# | 20 | 86 | |||||||
Spolverato (2015) [ [56] ] | 1990–2013 | 342 | ≥70 mm or mult. | 49 | 60 | – | 9 | ||||
N. AM, EUR, ASIA, AUS | multif | 85 | – | ||||||||
Tabrizian (2015) [ [43] ] | 1995–2011 | 81 | 51 | PAT | – | 11$ | 9 | ||||
US | singlek | 24 | |||||||||
Tian (2020) [ [44] ] | 2005–2011 | 168 | R0 | 41 | – | 20 | 12 (2) | ||||
CHI | singlei | 60 | – | 8 (>2) | 43 | ||||||
Tsilimigras (2020) [ [57] ]
A machine-based approach to preoperatively identify patients with the most and least benefit associated with resection for intrahepatic cholangiocarcinoma: an international multi-institutional analysis of 1146 patients. Ann Surg Oncol. 2020; 27: 1110-1119 | 1990–2017 | 1146 | 87§ | 44 | 60 | 18 | 13 (2–3) | 11 | |||
N. AM, EUR, ASIA, AUS | multif | 60 | – | 5 (>3) | |||||||
Wang T (2020) [ [45] ] | 2008–2018 | 398 | R0 | 47 | RAD‡ PAT† | 32 | 13 | ||||
CHI | singleh | 16 | 32 | ||||||||
Wang Y (2013) [ [46] ] | 2002–2007 | 367 | 33 | 53 | PAT | – | 89 (1–2) | 21 | |||
CHI | singlef | – | 11 (>2) | 51 | |||||||
Yeh (2015) [ [47] ] | 1977–2007 | 84 | R1+R2 | 26 | – | 10$ | |||||
TWN | single | 8 | |||||||||
Zhang (2017) [ [58] ] | 1990–2016 | 1023 | 80 | 44 | 59 | PAT | – | 17$ | 11 | ||
N. AM, EUR, ASIA, AUS | multif | 22 | |||||||||
Zhou (2011) [ [48] ] | 2005–2007 | 155 | 55# | – | 12$ | 29 | |||||
CHI | singlef | 25q | 56 | ||||||||
Zhu (2021) [ [49] ] | 2015–2018 | 126 | R0 | 94 | 59# | – | 10$ | ||||
CHI | single | 64# | 29 |
3.2 Demographic and clinicopathological data
3.3 Overall survival and surgical data
Study (year) | Overall md FU/md OS (months) | Md OS (months) Single/Multiple/Satellites | HR (95% CI) Multiple Yes vs No/Satellites Yes vs No/Other | HR (95% CI) Subgroup number of lesions [Subgroup vs Ref. group] | % Major hep./LND | % NAT/AT | % N1/R1/G3 |
---|---|---|---|---|---|---|---|
Addeo (2019) [ [18] ] | – | 36.5 | 1.8 (1.2–2.8) | 78 | 0 | 38 | |
28 | 21 | 2.2 (1.4–3.7)a [est] | 93 | 20 | 14 | ||
20 | 1.1 (0.5–2.3)c [est] | 22 | |||||
Aherne (2018) [ [32] ] | 41 | – | – | – | 8 | – | |
53 | – | 3.3 (1.4–8.0) | – | – | – | ||
– | NS (no data)d | – | |||||
Bartsch (2021) [ [19] ] | – | 27 | – | 75 | – | 28 | |
22 | – | 1.4 (0.7–3.1) [est] | – | – | 10 | ||
21.5 | 5.0 (1.8–13.8)c¶ [est] | 28 | |||||
Bednarsch (2021) [ [50] ] | 54 | 32 | – | 1.7 (0.9–3.1) [2–3 vs 1] | 76 | 9 | 37 |
25 | – | – | 1.7 (0.8–3.8) [4–5 vs 1] | – | 33 | 12 | |
– | 2.8 (1.5–5.4) [>5 vs 1] | 30 | |||||
Brustia (2020) [ [51] ] | – | – | – | 48 | 7 | 22 | |
26 | – | 1.7 (1.1–2.5) | – | 21 | 18 | ||
– | 27 | ||||||
Buettner (2017) [ [52] ] | – | – | 1.9 (1.5–2.3) | 1.8 (1.4–2.2) [2–3 vs 1] | 59 | – | 18 |
38 | – | – | 2.5 (1.7–3.6) [>3 vs 1] | 45 | – | 13 | |
– | – | – | |||||
Chen Q (2020) [ [33] ] | – | – | 1.4 (1.3-1-6)y | – | – | – | |
– | – | 1.7 (1.3–2.4) | – | – | – | ||
– | – | ||||||
Chen Y (2021) [ [59] ] | – | – | 1.6 (1.3–2.0) | 57 | 0 | 16 | |
– | – | 1.7 (1.4–2.1) | 33 | – | 0 | ||
20 | |||||||
Conci (2018) [ [53] ] | – | – | – | 60 | 8 | 26 | |
46 | – | 2.0 (1.2–3.3) [est] | – | 41 | 27 | ||
– | 5.1 (2.7–9.9)$ [est] | 36 | |||||
De Rose (2013) [ [54] ] | 26 | – | – | 70 | 0 | 24 | |
40 | – | P = 0.272†b | 77 | 53 | 19 | ||
– | 32 | ||||||
Fu (2020) [ [34] ] | 55 | – | 6.1 (3.5–10.8) | – | – | 32 | |
– | – | – | – | – | 42 | ||
– | 9.3 (4.9–17.7)d | 12 | |||||
Kim (2014) [ [35] ] | – | – | – | 86 | – | – | |
– | – | – | – | – | 16 | ||
– | 1.9 (0.3–14.3)$ | – | |||||
2.1 (1.6–6.7)d @ | |||||||
King (2020) [ [36] ] | – | – | – | 0 | 25 | ||
– | – | 1.9 (0.9-4.1)o | – | – | – | ||
– | 49 | ||||||
Kudo (2021) [ [60] ] | – | 64 | – | – | 64& | – | 22& |
– | 35++ 17f | – | – | 49& | – | 6& | |
– | – | ||||||
Lee (2014) [ [37] ] | – | – | – | – | – | 23 | |
34 | – | 1.8 (0.9–3.4) | – | – | 8 | ||
– | 85 | ||||||
Liu (2018) [ [38] ] | 29 | – | – | 0.8 (0.5–1.3) [2–3 vs 1] | – | – | 17 |
– | – | – | 2.7 (1.7-4.2) [>3 vs 1] | – | – | – | |
– | 38 | ||||||
Lu (2016) [ [39] ] | – | – | – | – | – | – | |
– | – | P = 0.006 | – | – | – | ||
– | – | ||||||
Moro (2019) [ [55] ] | – | – | 1.2 (1.2–1.3)? | 62 | 6 | 16 | |
21 | – | 2.1 (1.8–2.6) | – | 28 | 14 | ||
– | 19 | ||||||
Nuzzo (2010) [ [40] ] | – | 29 | P = 0.285 | P = 0.089 [>3 vs ≤ 2] | 78 | – | 26 |
30 | 21 | – | 73 | – | 20 | ||
– | 32 | ||||||
Scheuermann (2013) [ [41] ] | – | – | – | P = 0.030 [>3 vs ≤ 3] | 81 | – | 29 |
28 | – | – | – | 37 | 27 | ||
– | 31 | ||||||
Shen (2018) [ [42] ] | – | – | – | – | – | 20 | |
18 | – | 2.4 (1.3–4.5) [est] | – | – | 8 | ||
– | 2.4 (1.0–6.1)$ [est] | 73 | |||||
Spolverato (2015) [ [56] ] | 22e | – | – | 1.7 (1.2–2.4) [>3 vs ≤ 3] | 76p | – | 21 |
21 | – | – | 56 | 51 | 19 | ||
29 | |||||||
Tabrizian (2015) [ [43] ] | 27 | 41 | – | 73 | – | 26 | |
– | 27 | 2.1 (1.1–4.1) [est] | – | 28 | 24 | ||
24 | 1.9 (0.8–4.7)$ [est] | 40 | |||||
Tian (2020) [ [44] ] | 45e | – | 1.2 (0.6–1.3) [2 vs 1] | 0 | 5 | ||
28e | – | – | – | – | 0 | ||
– | 30 | ||||||
Tsilimigras (2020) [ [57] ]
A machine-based approach to preoperatively identify patients with the most and least benefit associated with resection for intrahepatic cholangiocarcinoma: an international multi-institutional analysis of 1146 patients. Ann Surg Oncol. 2020; 27: 1110-1119 | – | – | – | Md OS months (95% CI): 15 (10–19) vs 21 (16–27) [>3 vs 2–3] | 61 | 7 | 17 |
– | 21+++ 15g | – | – | – | 13 | ||
– | 18 | ||||||
Wang T (2020) [ [45] ] | – | – | 1.8 (1.3–2.3) | 61 | – | 25 | |
– | – | 1.7 (1.3–2.4) | – | – | 0 | ||
– | – | ||||||
Wang Y (2013) [ [46] ] | 39 | – | – | 1.4 (0.9–2.2) [2–3 vs. 1] | – | – | 22 |
21 | – | – | – | – | – | ||
– | – | ||||||
Yeh (2015) [ [47] ] | – | 9.5 | – | – | – | 31 | |
– | 10.5 | – | – | 46 | 100μ | ||
4 | 29 | ||||||
Zhang (2017) [ [58] ] | – | – | – | 59 | – | 17 | |
37 | – | 2.1 (1.7–2.6) | 47 | – | 13 | ||
– | 2.0 (1.6–2.5)$ | 17 | |||||
Zhou (2011) [ [48] ] | – | – | – | – | 21 | ||
17 | – | P < 0.001n | – | – | – | ||
– | P = 0.007$ | 24 | |||||
Zhu (2021) [ [49] ] | 18 | – | – | – | 1 | 26 | |
29 | – | 1.5 (0.7–2.0) | 63 | 34 | 0 | ||
– | 2.1 (1.0–4.2$ | 41 |
3.4 Systematic review: prognostic influence of number of lesions
- Tsilimigras D.I.
- Mehta R.
- Moris D.
- Sahara K.
- Bagante F.
- Paredes A.Z.
- et al.
- Tsilimigras D.I.
- Mehta R.
- Moris D.
- Sahara K.
- Bagante F.
- Paredes A.Z.
- et al.
3.5 Systematic review: prognostic influence of other categories of tumour distribution
3.6 Meta-analysis: prognostic influence of satellite lesions

3.7 Meta-analysis: prognostic influence of multiple lesions not including satellites

3.8 Assessment of evidence
3.9 Multivariable comparisons
4. Discussion
- Baheti A.D.
- Tirumani S.H.
- Shinagare A.B.
- Rosenthal M.H.
- Hornick J.L.
- Ramaiya N.H.
- et al.
Sources of funding and role of the funding source
Data availability statement
CRediT authorship contribution statement
Conflict of interest disclosure
Acknowledgements
Appendix A. Supplementary data
- Multimedia component 1
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