Difficulties for the analysis of rare cancers
• Missing casesExample: angiosarcoma of liver
• Including false casesExample: malignant digestive endocrine
tumours
Difficulties for the technician
to record rare cancers
- Unusual morphology in pathologic records seems commonly easy to identify for technicians
- Pathologists themselves are not experts in these cancers the conclusion of their reports may often be not clear
It may be difficult to identify the morphologic code corresponding to these cases
IARC SCIENTIFIC PUBLICATIONS, 1997 Chapter 4. Histological groupsD.M. Parkin, J. Ferlay, K. Shanmugaratnam, L. Sobin, L. Teppo and S.L. Whelan
Example: angiosarcoma of liver: very rare and not well known
Various codes in the literature , ~ 200 annual new cases per year …?
Example : How to choose the good code for angiosarcoma of liver ?
Survival from rare cancer in adults: a population-based studyThe Lancet Oncology, 2006
Morphologic codes accepted whatever the topography Angiosarcoma ?
(8800/3) Sarcoma
(8804/3) Epithelioid sarcoma
(8850/3) Liposarcoma
(8860/3) Angio myoliposarcoma
(8890/3) Leiomyosarcoma
(8894/3) Angiomyosarcoma
(8895/3) Myosarcoma
(8900/3) Rhabdomyosarcoma
(9120/3) Hemangiosarcoma
(9124/3) Küpfer cell sarcoma (C22)
(9130/3) Hemangioendothelioma
(9133/3) Epithelioid hemangioendothelioma
(9140/3) Kaposi's sarcoma
(9161/3) Hemangioblastoma
Example : How to choose the good code for angiosarcoma of liver ?
Survival from rare cancer in adults: a population-based studyThe Lancet Oncology, 2006
Morphologic codes accepted whatever the topography Angiosarcoma ?
(8800/3) Sarcoma no
(8804/3) Epithelioid sarcoma no
(8850/3) Liposarcoma no
(8860/3) Angio myoliposarcoma no
(8890/3) Leiomyosarcoma no
(8894/3) Angiomyosarcoma no
(8895/3) Myosarcoma no
(8900/3) Rhabdomyosarcoma no
(9120/3) Hemangiosarcoma yes
(9124/3) Küpfer cell sarcoma (C22) yes
(9130/3) Hemangioendothelioma yes
(9133/3) Epithelioid hemangioendothelioma yes
(9140/3) Kaposi's sarcoma no
(9161/3) Hemangioblastoma yes ?
Proposal for rare cancers
• Identification in the database of rare cancers by a inedited specific code (XXXX)
• Precise codification of the morphologic code in a second variable after validation by a pathologist and/or a clinician
This aims in an:
- improvement in the identification and the extraction of rare cancers- improvement in the quality of data
Malignant digestive endocrine tumours (mdet)
• Not so rare (~ 0.8/100 000)
• But difficulties with the rules of codification :
– Heterogeneity of this group of cancer arising from diverse sites
– How to distinguish between benign and malignant tumours
– Changes in the classification over time
Well differentiated
benign ET
Well differentiatedborderline ET
Well differentiatedendocrine carcinoma
undifferentiated endocrine
carcinoma
Differenciation Well differentiated Well differentiated Well differentiated Undifferentiated
Angioinvasion No Possible Possible Possible
Size Stomach, Small intestine: < 1cm Appendix, colon, rectum : < 2 cm Pancreas : < 2 cm
Stomach, Small intestine : >1 cm
Appendix, colon, rectum : > 2 cm Pancreas : > 2cm
Stomach, Small intestine : >1 cm
Appendix, colon, rectum : > 2 cm
Pancreas : >3 cmo
Mitotic Index < 2 Pancreas > 2 2 to 10 > 10
Proliferation Index (Ki67)
< 2 % often > 2 % 2 to 15 % > 15 %
local invasion Digestive tumour :mucosae/submucosae Pancreas : intra-pancreatic
Digestive tumour :mucosae/muscularis propria Pancreas : intra-pancreatic
Digestive tumour (out appendix):> Muscularis propria Appendix : invasion of the visceral peritoneumPancreas : extra-pancreatic extension
Metastases no no Possible Possible/ 3Behaviour : / 2
Mdet : difficulties with the rules of codification
Example : colon cancers. High frequency of appendix mdet, usually benign
country
hepatic
flexure transverse lef t colon caecum appendix ascending nos Total
AUSTRI A 0 0 2 5 11 1 18 37
CZECH REPUBLI C 1 0 2 1 0 2 1 7
DENMARK 1 7 26 78 64 26 8 210
ENGLAND 13 19 41 204 259 29 100 665
ESTONI A 0 5 2 15 6 8 9 45
FI NLAND 0 0 14 0 0 0 11 25
FRANCE 3 1 11 18 8 13 3 57
GERMANY 0 2 3 10 12 0 15 42
I CELAND 0 0 3 5 0 3 1 12
I TALY 2 4 13 32 29 12 12 104
MALTA 0 0 0 1 0 1 0 2
NORWAY 104 8 14 0 0 66 4 196
POLAND 1 0 2 1 4 0 3 11
PORTUGAL 0 0 1 1 0 0 1 3
SCOTLAND 2 1 12 46 55 7 15 138
SLOVAKI A 1 5 10 29 27 6 0 78
SLOVENI A 16 2 1 0 0 0 1 20
SPAI N 1 0 4 12 17 4 3 41
SWEDEN 0 24 35 0 0 284 76 419
SWI TZERLAND 0 0 1 6 6 2 0 15
THE NETHERLANDS 1 1 10 23 177 6 2 220
WALES 0 0 0 12 17 1 13 43
Eurocare, 1985-1994
Example : colon cancers. High frequency of appendix mdet, usually benign
country
hepatic
flexure transverse lef t colon caecum appendix ascending nos Total
AUSTRI A 0 0 2 5 11 1 18 37
CZECH REPUBLI C 1 0 2 1 0 2 1 7
DENMARK 1 7 26 78 64 26 8 210
ENGLAND 13 19 41 204 259 29 100 665
ESTONI A 0 5 2 15 6 8 9 45
FI NLAND 0 0 14 0 0 0 11 25
FRANCE 3 1 11 18 8 13 3 57
GERMANY 0 2 3 10 12 0 15 42
I CELAND 0 0 3 5 0 3 1 12
I TALY 2 4 13 32 29 12 12 104
MALTA 0 0 0 1 0 1 0 2
NORWAY 104 8 14 0 0 66 4 196
POLAND 1 0 2 1 4 0 3 11
PORTUGAL 0 0 1 1 0 0 1 3
SCOTLAND 2 1 12 46 55 7 15 138
SLOVAKI A 1 5 10 29 27 6 0 78
SLOVENI A 16 2 1 0 0 0 1 20
SPAI N 1 0 4 12 17 4 3 41
SWEDEN 0 24 35 0 0 284 76 419
SWI TZERLAND 0 0 1 6 6 2 0 15
THE NETHERLANDS 1 1 10 23 177 6 2 220
WALES 0 0 0 12 17 1 13 43
30%
40%
80%
39%
Eurocare, 1985-1994
small cell mdet well- diff erentiated total
AUSTRI A 1 89 90
CZECH REPUBLI C 8 32 40
DENMARK 119 713 832
ENGLAND 757 1,955 2,712
ESTONI A 64 121 185
FI NLAND 0 1,56 1,56
FRANCE 8 332 340
GERMANY 35 168 203
I CELAND 8 105 113
I TALY 81 374 455
MALTA 1 12 13
NORWAY 32 923 955
POLAND 20 23 43
PORTUGAL 3 4 7
SCOTLAND 199 415 614
SLOVAKI A 54 204 258
SLOVENI A 19 63 82
SPAI N 40 131 171
SWEDEN 0 3,943 3,943
SWI TZERLAND 12 133 145
THE NETHERLANDS 42 419 461
WALES 42 122 164
Example : mdet Eurocare. Information on the differentiation, a major pronostic factor
small cell mdet well- diff erentiated total
AUSTRI A 1 89 90
CZECH REPUBLI C 8 32 40
DENMARK 119 713 832
ENGLAND 757 1,955 2,712
ESTONI A 64 121 185
FI NLAND 0 1,56 1,56
FRANCE 8 332 340
GERMANY 35 168 203
I CELAND 8 105 113
I TALY 81 374 455
MALTA 1 12 13
NORWAY 32 923 955
POLAND 20 23 43
PORTUGAL 3 4 7
SCOTLAND 199 415 614
SLOVAKI A 54 204 258
SLOVENI A 19 63 82
SPAI N 40 131 171
SWEDEN 0 3,943 3,943
SWI TZERLAND 12 133 145
THE NETHERLANDS 42 419 461
WALES 42 122 164
Example : mdet Eurocare. Information on the differentiation, a major prognostic factor
Proposal Before the final analysis:
•To ensure that homogenous rules are used between registries
•To compare incidence and relative survival ratesby subsite, by morphology, by period…
in order to mark possible disparities in the registration between countries and between registries in the same registry
conclusion
There is a need to ask each registry their rules of codification before analysing rare cancers cases