Radiotherapy Utilisation Rates

M Barton, G Delaney

J Shafiq, S Jacob, S Thompson, K Wong

T Hanna Funded by Department of Health and

Ageing, Government of Australia

Indications for radiotherapy

• Superior clinical outcome

– survival

– local control

– toxicity profile

• and the patient is suitable

– performance status

– co-morbidities.

Tree design

RT

Good PS

No RT

Poor PS

Localised disease

RT

symptoms for RT

Good PS

NO RT

Poor PS

Metastatic disease

Example cancer

• Type/site

• Stage

• Performance status

• Relapse

0.56

RT

Good PS (0.70)

No RT

Poor PS (0.30)

Localised disease (0.80)

0.06

RT

symptoms for RT (0.50)

Good PS (0.60)

NO RT

Poor PS (0.40)

Metastatic disease (0.20)

Example cancer

• 1000+ Pieces of evidence examined

• Constructed RTU trees on 23/23 cancers

• Comprises 98% of all cancer by incidence

• 52% of all cancers need RT

RTU 2003

Zimbabwe

Zambia

Yemen Vietnam

Venezuela

Uzbekistan

Uruguay

United States

United Kingdom

United Arab Emirates

Ukraine

Uganda

Turkey

Tunisia

Thailand

Tanzania

Tajikistan

Syria

Sudan

Sri Lanka

Spain

South Africa

Slovenia

Slovakia

Singapore

Sierra Leone

Serbia

Senegal

Saudi Arabia

Russia

Romania

Qatar

Portugal

Poland

Philippines

Peru

Paraguay

Panama

Pakistan

Nigeria

Niger

Nicaragua

Namibia

Myanmar

Morocco

MongoliaMoldova

Mexico

Mauritius

Malta

Mali

Malaysia

Malawi

Madagascar

Macedonia

Lithuania

Libya

Liberia

Lebanon

Latvia

Kyrgyzstan

Kuwait

Korea Rep.

Korea DPR

Kenya

Kazakhstan

Jordan

Japan

Jamaica

Ireland

Iraq

Indonesia

India

Iceland

Hungary

Honduras

Haiti

Guatemala

Greece

Ghana

Germany

Georgia

Gabon

France

Ethiopia

Estonia

EritreaEl Salvador

Egypt

Ecuador

Dominican Republic

Democratic Republic of the Congo

Czech Republic

Cyprus

Cuba

Croatia

Cote D'Ivoire

Costa Rica

China

Chile

Chad

Central African Republic

Cape Verde

Canada

Cameroon

Burkina Faso

Bulgaria

Brazil

Botswana

Bosnia and Herzegovina

Bolivia

Benin

Belarus

Bangladesh

Azerbaijan

Australia

Armenia

Argentina

Angola

Algeria

Albania

Afghanistan

% of cases requiring RT

0 - 0.39

0.39 - 0.43

0.43 - 0.47

0.47 - 0.51

0.51 - 0.55

0.55 - 0.59

0.59 - 1 N. Taylor, IAEA 2005

Other studies

Published

• Chemotherapy

• Brachytherapy

• Genetic services

• Screen-detected breast cancer

• Survival benefit breast

• Adaption to LMI countries

• Palliative RT

• Fractionation

On going

• Travel distance

• Survival and local control – RT

– Chemotherapy

• Integration with economics

Peer-reviewed publications 1. G Delaney, MB Barton, S Jacob, B Jalaludin. A model for decision making for the use of

radiotherapy in lung cancer. Lancet Oncology 2003; 4 : 120 – 128 2. G Delaney, MB Barton. S Jacob. Estimation of an optimal radiotherapy utilization rate for breast

cancer: A review of the evidence. Cancer 2003 Oct; 98;1977-1986. 3. MB Barton, S Jacob, V Gebski. Utility-adjusted analysis of the cost of palliative radiotherapy for

bone metastasis. Australas Radiol. 2003 Sep;47(3):274-8. 4. G Delaney, MB Barton. S Jacob. Estimation of an optimal radiotherapy utilization rate for

melanoma: A review of the evidence. Cancer. 2004 Mar 15;100(6):1293-301. 5. G Delaney, MB Barton. S Jacob. Estimation of an optimal radiotherapy utilization rate for

gynaecological cancers Part I: Malignancies of the Cervix, Ovary, Vagina, and Vulva. A review of the evidence. Cancer. 2004;101:671–81

6. G Delaney, S Jacob, MB Barton. Estimation of an optimal radiotherapy utilization rate for gynaecological cancers Part II: Carcinoma of the endometrium. A review of the evidence. Cancer. 2004;101:682–92

7. G Delaney, S Jacob, MB Barton. Estimation of an optimal radiotherapy utilization rate for gastro-intestinal cancers: A review of the evidence. Cancer. 2004;101:657–70

8. C Featherstone. G Delaney, S Jacob, MB Barton. Estimation of an optimal radiotherapy utilization rate for haematological malignancies (Part I): A review of the evidence. Cancer 2005 Jan 15; 103(2):383-92.

9. C Featherstone. G Delaney, S Jacob, MB Barton. Estimation of an optimal radiotherapy utilization rate for haematological malignancies (Part II): A review of the evidence. Cancer 2005 Jan 15;103(2):393-401.

10. G Delaney, S Jacob, MB Barton. Estimating the optimal external-beam radiotherapy utilization rate for genito-urinary cancers: A review of the evidence. Cancer. 2005 Feb 1;103(3):462-473

11. G Delaney, S Jacob, MB Barton. Estimating the optimal external-beam radiotherapy utilization rate for head and neck cancers: A review of the evidence. Cancer. 2005 Jun 1;103(11):2216-27.

12. G Delaney, S Jacob, C Featherstone and MB Barton. The Role of Radiotherapy in the Treatment of Cancer: Estimating the optimal utilization from a review of evidence-based clinical guidelines. Cancer 2005 Sep 15; 104(6): 1129-37

13. G Delaney, S Jacob, MB Barton. Estimating the optimal radiotherapy utilization for cancer of the central nervous system, thyroid cancer and cancer of unknown primary origin from evidence-based clinical guidelines. Cancer 2006 Jan 15;106(2):453-65.

14. MB Barton, M Frommer, RJ Shafiq. The role of radiotherapy in cancer control in low and middle income countries. Lancet Oncology 2006, 7 (7): 584-595.

15. Jacob S, Ng W, Delaney GP, Barton MB. Estimation of an optimal chemotherapy utilisation rate for primary malignant brain tumours: an evidence-based benchmark for cancer care. Clin Oncol (R Coll Radiol). 2011 Feb;23(1):48-54. S Vinod, MB Barton. Actual versus optimal utilisation of radiotherapy in lung cancer – Where is the shortfall? Asia Pacific Journal of Clinical Oncology.2007; 3: 1- 7.

17. C Featherstone, A Colley, K Tucker, J Kirk, MB Barton. Estimating the referral rate for cancer genetic assessment from a systematic review of the evidence. Br. J. Cancer 2007: 96:391-398.

18. J Shafiq, GP Delaney, MB Barton. An evidence-based estimation of local control and survival benefit of radiotherapy for breast cancer. Radiother Oncol. 2007 Jul;84(1):11-7.

16. 19. F Hegi-Johnson, GS Gabriel, A Kneebone, S.K C Wong, B Jalaludin, S Behan, and MB Barton. Utilisation of Radiotherapy for Rectal Cancer in Greater Western Sydney 1994-2001. ANZ J Surg. 2007;3:134-142

20. M Alam, G Gabriel, M Barton, R Eek. Discriminating factors in treatment decisions for chemotherapy in elderly patients with colorectal cancer. Cancer Forum, 2008, 32 (1): 22-26.

21. G Delaney, J Shafiq, G Chappell and M Barton. Establishing treatment benchmarks for mammography-screened breast cancer population based on a review of evidence-based clinical guidelines. Cancer. 2008 May 1;112(9):1912-22

22. S Thompson, G Delaney, GS Gabriel, S Jacob, P Das, MB Barton. Estimation of the Optimal Brachytherapy Utilization Rate in the Treatment of Malignancies of the Uterine Corpus by a Review of Clinical Practice Guidelines and Primary Evidence. Int. J. Radiation Oncology Biol. Phys., Vol. 72, No. 3, pp. 849–858, 2008.

23. S Jacob, W Ng, R Ashgari, G Delaney, M Barton. Estimation of an optimal chemotherapy utilisation rate for colon cancer: an evidence-based benchmark for cancer care. Eur J Cancer 45 (2009) 2503 - 2509.

24. W Ng, S Jacob, G Delaney, M Barton. Estimation of an optimal chemotherapy utilisation rate for head and neck carcinoma: setting an evidence-based benchmark for the best-quality cancer care. Eur. J Cancer 2009 Aug;45(12):2150-9

25. S Jacob, K Wong, P Adams, GP Delaney, MB Barton. Estimation of an Optimal Utilisation Rate for Palliative Radiotherapy in Newly Diagnosed Cancer Patients. Clinical Oncology 22 (2010) 56–64.

26. W Ng, G Delaney, S Jacob, M Barton. Estimation of an optimal chemotherapy utilisation rate for breast cancer: Setting an evidence-based benchmark for the best-quality cancer care. Eur J Cancer. (2010) Mar;46(4):703-12.

27. S Jacob, E Hovey, W Ng, S Vinod, GP Delaney and MB Barton. Estimation of an optimal chemotherapy utilisation rate for lung cancer: An evidence-based benchmark for cancer care. Lung Cancer 69 (2010) 307–314.

28. A Fong, W Ng, MB Barton, G Delaney. Estimation of an evidence-based benchmark for the optimal endocrine therapy utilization rate in breast cancer. Breast. 2010 Oct; 19(5):345-9.

29. Jacob S, Ng W, Delaney GP, Barton MB. Estimation of an optimal chemotherapy utilisation rate for primary malignant brain tumours: an evidence-based benchmark for cancer care. Clin Oncol (R Coll Radiol). 2011 Feb;23(1):48-54.

30. MB Barton, GP Delaney. A decade of investment in radiotherapy in NSW; why does the gap between optimal and actual service delivery persist? Journal of Medical Imaging and Radiation Oncology 55 (2011) 433–441

31. PL Blinman, P Grimison, MB Barton, S Crossing, E Walpole, N Wong, K Francis, B Koczwara. The shortage of medical oncologists in Australia: the Australian Medical Oncology Workforce Study. MJA 2012; 196: 58–61

32. Thompson SR, Delaney GP, Gabriel GS, Jacob S, Barton MB. Estimation of the optimal brachytherapy utilisation rate in the treatment of vaginal cancer and comparison with patterns of care. J Med Imaging Radiat Oncol. 2012 Aug;56(4):483-9.

33. A Fong, J Shafiq, C Saunders, A Thompson, S Tyldesley, IA Olivotto, MB Barton, JA Dewar, S Jacob, W Ng, C Speers. A comparison of systemic breast cancer therapy utilization in British Columbia, Scotland, and Western Australia with models of “optimal” therapy. The Breast 2012 Aug;21(4):562-9.

34. Fong A, Shafiq J, Saunders C, Thompson AM, Tyldesley S, Olivotto IA, Barton MB, Dewar JA, Jacob S, Ng W, Speers C, Delaney GP. A comparison of surgical and radiotherapy breast cancer therapy utilization in Canada (British Columbia), Scotland (Dundee), and Australia (Western Australia) with models of "optimal" therapy. Breast. 2012 Aug;21(4):570-7.

35. S Thompson, GP Delaney, GS Gabriel, S Jacob, P Das and MB Barton. Estimation of the Optimal Brachytherapy Utilization Rate in the Treatment of Gynaecological Cancers and Comparison with Patterns of Care. Int J Radiation Oncol Biol Phys, Vol. 85, No. 2, pp. 400-405, 2013

RTU revision 2012

• External beam radiotherapy

• Synchronous chemotherapy

• Brachytherapy

• Patient preference

RTU revision 2012 Site Old RTU New RTU Changed indications

Bladder 58% 47% 0

Brain 92% 80% Tree changed

Breast 83% 87% 0

Cervix 58% 71% Tree changed

Colon 14% 4% -1

Gall bladder 13% 17% 0

Head and Neck 74% 74% Tree changed

Kidney 28% 15% -1

Leukaemia 4% 4% 0

Liver 0% 0% N/A

Lung 76% 77% 0

Lymphoma 65% 73% 4

Melanoma 23% 21% 3

Myeloma 38% 45% 3

Site Old RTU New RTU Changed indications

Oesophagus 80% 71% 1

Other 50% 19% Tree changed

Ovary 4% 4% 0

Pancreas 57% 49% Tree changed

Prostate 60% 58% Tree changed

Rectum 65% 60% 0

Stomach 68% 27% 1

Testis 49% 7% Tree changed

Thyroid 10% 4% Tree changed

Unknown Primary 61% 61% 0

Uterus 46% 38% Tree changed

Vagina 100% 94% Tree changed

Vulva 34% 39% Tree changed

TOTAL 52.3% 48.3%

Indications

Added • Lymphoma

• Melanoma

• Myeloma

• Oesophagus

• Stomach

Removed • Colon

• Kidney

Radiotherapy utilisation rates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Old RTU New RTU

Absolute Percent Change to RTU for all Cancers by Site

2012 versus 2003 Model

-5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5%

TOTAL (all cancer)

Unknown Primary

Colon

Stomach

Bladder

Rectum

Brain

Lung

Head and Neck

Kidney

Melanoma

Testis

Breast

Uterus

Pancreas

Other

Gall bladder

Thyroid

Leukaemia

Ovary

Vagina

Liver

Vulva

Oesophagus

Cervix

Myeloma

Lymphoma

Prostate

Only change in AIHW data AIHW changes plus other changes

Synchronous Chemoradiotherapy

Tumour Site

Proportion of Notifiable Cancers

Optimal CT-RT Utilization Rate

Proportion of cancers with CT-RT indication

Bladder 2.0% 9% 0.2%

Brain 1.4% 53% 0.7%

Cervix 1.0% 51% 0.5%

Gall bladder 0.6% 17% 0.1%

Head and Neck 3.3% 26% 0.8%

Lung 9.0% 26% 2.3%

Oesophagus 1.2% 33% 0.4%

Pancreas 2.1% 35% 0.7%

Rectum 4.2% 55% 1.9%

Stomach 1.8% 20% 0.3%

Vagina 0.1% 78% 0.08%

Other 5.0% 5% 0.3%

Total 31.7% 28% 8.9%

Optimal Brachytherapy Utilization

Tumour Site Proportion of Notifiable Cancers %

Optimal BT Utilization

Rate %

Optimal BT Utilization Rate for all Cancers %

Cervix 1 53 0.5

Melanoma 10 2 0.2

Prostate 18 10 1.8

Uterine Corpus 2 39 0.7

Vagina 0.1 85 0.1

Total 31 11% 3.3%

Stephen Thompson

Sensitivity analysis

• Univaritate

– 150 variables

• Multivariate

– Monte Carlo simulation

Utilisation rate Range

RT 48.3% 47.9% 48.7%

CT RT 8.9% 8.5% 9.3%

Brachytherapy 3.3% 3.0% 3.3%

Tornado Diagram at

Cancer

Expected Value

0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.50 0.50 0.50

pPref er_EBRTv sRP: 0.25 to 0.75

pPref er_RTv sRP: 0.25 to 0.75

pPref er_BTv sEBRT: 0.25 to 0.75

p_stomach_adjCRT_indicated: 0. to 1.

p_my eloma_bonepain_bispohphonate: 0.11 to 0.42

p_kidney _distant_mets: 0.23 to 0.58

pSBTM_indicatedGoodIR: 0. to 1.

p_pancreas_stageI_II_inop_CRT: 0. to 1.

p_unknown_primary_node_mets: 0.37 to 0.52

p_breast_stageI_II_1_3nodes: 0. to 1.

p_unknown_primary_bone_mets: 0.17 to 0.33

p_pancreas_adjuv ant_radiotherapy : 0. to 1.

p_colon_M1_unresectable: 0. to 0.11

p_gallbladder_unresectable_RT: 0. to 1.

p_melanoma_node_brain_bone_mets: 0.21 to 0.50

p_oesophagus_preop_RT: 0. to 1.

p_pancreas_stageIII_y oung: 0. to 1.

p_lip_adverse_f eatures: 0.07 to 0.34

p_v ulv al_stageI_II_no_surgery _CRT: 0. to 1.

Proportion of stage IV bladder cancer with bone metastases: 0.17 to 0.43

p_melanoma_RT_large_node: 0. to 1.

Proportion of patients getting surgery f or Stage II-III bladder cancer: 0.30 to 0.60

pLRDx_BT_Phy sically CI: 0.33 to 0.62

p_seminoma_st_I_radiotherapy : 0. to 0.445

p_melanoma_RT_ENS: 0. to 1.

p_lip_surgery : 0.81 to 0.99

pBone_metsbreast: 0.42 to 0.71

p_oroph_HPVpositiv eRRT: 0.24 to 0.84

p_ov arian_stageIV_metastasis: 0.12 to 0.18

p_unknown_primary_brain_mets: 0.03 to 0.09

p_lary nx_laser: 0. to 0.23

p_NSCLC_early __distant_relapse: 0.16 to 0.34

p_oesophagus_neoadjuv ant: 0.31 to 0.76

pSurgery ForStageI_VaginalCancer: 0.11 to 0.32

p_colon_T4_RT: 0. to 0.055

p_NHL_low_grade_CR: 0.38 to 0.66

p_oroph_HPVnegativ eRRT: 0.24 to 0.84

Proportion of stage IV bladder cancer with brain metastases: 0.01 to 0.12

p_NSCLC_lr_stageIIIa_updated: 0.09 to 0.38

p_oral_cav ity _surgery _StI_II: 0.76 to 0.90

p_cerv ix_Medically Inoperable: 0.04 to 0.19

p_NSCLC_surgery_margins_updated: 0.01 to 0.05

p_relapse_ALL_child: 0.12 to 0.46

p_gallbladder_resectable: 0.45 to 0.59

p_oral_cav ity _III_IVB_no_adjuv RT: 0. to 0.08

p_kidney _M1_sy mptomatic_primary : 0. to 0.06

p_thy roid_papillary_distant_recur: 0.04 to 0.11

p_oesophagus_bone_mets: 0.16 to 0.33

VRwithoutDM: 0. to 0.29

p_SCLC_brain_mets: 0.16 to 0.26

p_gallbladder_metastatic: 0.38 to 0.47

pSurgery ForStageII_VaginalCancer: 0.07 to 0.20

p_v ulv al_stageIII_IVA_no_surgery _CRT: 0. to 1.

p_NSCLC_StageIIIA_distant: 0.32 to 0.59

p_oesophagus_resectable: 0.53 to 0.80

p_cerv ix_LN: 0.73 to 0.80

p_adult_AML_complete_remission: 0.55 to 0.75

p_AML_adult_relapse: 0.43 to 0.60

p_v ulv al_local_recurrence: 0.15 to 0.33

p_v ulv al_node_positiv e: 0.57 to 0.76

p_v ulv al_recurrent_RT: 0.23 to 0.38

p_oesophagus_lc: 0.21 to 0.68

p_lary nx_sg_conserv ativ e_surgery : 0. to 0.23

p_CNS_ALL_child: 0.03 to 0.07

p_thy roid_papillary_bone_mets: 0.19 to 0.30

p_headandneck_N1_2a_disease: 0.09 to 0.22

p_adult_ALL_complete_remission: 0.78 to 0.93

p_NHL_low_MALT_CR_to_HP: 0.56 to 0.81

p_thy roid_papill_local_recur: 0.03 to 0.15

p_oesophagus_postop_recurrence: 0.40 to 0.50

p_seminoma_st_III_resid_disease: 0. to 0.15

p_oroph_HPVpositiv e: 0.66 to 0.80

p_testicular_ca_brain_mets_RT: 0. to 1.

p_seminoma_st_II_resid_disease: 0. to 0.07

pIRDx_BT_Phy sically CI: 0.20 to 0.52

pHRDx_BT_Phy sically CI: 0.29 to 0.61

p_rectum_resectable_CRTindicated: 0. to 1.

p_oesoph_distant_mets: 0.18 to 0.30

p_melanoma_node_positiv e: 0.22 to 0.55

p_melanoma_H_N: 0.16 to 0.20

p_unknown_primary_CRT: 0. to 1.

p_paranasal_operableRRT: 0. to 0.62

p_oroph_HPVpositiv e_adjRT: 0.31 to 0.36

p_oroph_HPVnegativ e_adjRT: 0.56 to 0.63

p_hy pophary nx_CRTrate: 0.50 to 0.81

p_cerv ix_Margin: 0.10 to 0.60

p_breast_v ary _node_inv olv ement: 0.18 to 0.34

Factors that affect Patient choice

• Treatment

– Benefits, toxicity, previous experience

• Socio-demographic

– Age, sex, marriage, dependents

• Clinical

– Type, stage, LN, recurrence

• Methodology

– Framing, order of questions, starting point

• Time

• Cognitive/affective

– Belief in tmt benefits, need to act, regret

• Provider

Actual utilisation

Journal of Medical Imaging and Radiation Oncology 55 (2011) 433–441

Actual utilisation

Raw data RT 34,442

32% New cancers 108,064

Data linkage 2004 -2006

RT 25,917 24%

New cancers 108,064

Data Linkage + GIS

RT 24,952 26%

New cancers 96,803

NSW

ACT

QLD

SA

NSW

VIC

ACT

• NSW 2004 - 2006 • Optimal RTU at

diagnosis = 44%

21

P < 0.001

0%

5%

10%

15%

20%

25%

30%

0- km 50- km 75- km 100-km

125-km

150-km

200-km

250-km

300-km

350-km

400+km

Effect of travel distance on RTU in NSW & ACT (2004-06)

P<0.001

Appropriateness

-2000 -1000 0 1000 2000 3000 4000

Radiotherapy

Chemotherapy

Hormone therapy

Screen-detected breast cancer

Recommended &received

Not recommended &not received

Not recommended butreceived

Recommended butnot received

CANCER May 1, 2008, 112 (9): 1912 - 1922

Conclusions

Optimal utilisation

• 48.3% RT

• 8.9% CTRT

• 3.3% BT

Actual utilisation

• Discrepancies between methods

• Appropriateness

Estimating cost and benefit Melanoma

Mucosal

0.0100.01 0.0083

Ocular

0.0200.02

Surgery

0.9300.07

No surgery /inadequate m argin

0.0700.01 0.0980

Lentigo Maligna Melanom a

0.0800.069 / 0.7

Desm oplastic

0.0200.02 0.0455

No recurrence

0.9800.44

Nodal recurrence

0.9000.01 0.0000

Brain mets

0.0800.00 0.0000

Bone m ets

0.0200.00 0.0000

Nodal/brain/bone recurrence

0.0200.000 / 18.4

Stage I

0.5600.000 / 0.4

Nodal recurrence

0.9000.00 0.0000

Brain mets

0.0800.00 0.0000

Bone m ets

0.0200.00 0.0000

Nodal/ brain/ bone recurrence

0.2500.000 / 18.4

No bone/ brain/ nodal recurrence

0.7500.01

No ENS

0.5000.000 / 4.6

Extra-nodal spread (ENS)

0.5000.01 0.0070

Node size <=4cm

0.6100.070 / 12.3

Node size >4cm

0.3900.01 0.0070

Single node involved

0.4500.097 / 15.3

Multiple nodes involved

0.5500.05 0.0070

Node positive

0.2400.121 / 17.9

No recurrence

0.9200.04

Nodal recurrence

0.9000.00 0.0000

Brain mets

0.0800.00 0.0000

Bone m ets

0.0200.00 0.0000

Nodal/ brain/ bone recurrence

0.5100.000 / 18.4

No bone/ brain/ nodal recurrence

0.4900.00

Nodal /sy stemic recurrence

0.0800.000 / 9.4

pT1-3

0.8400.000 / 0.8

pT4

0.1600.01 0.0090

Head and neck

0.2100.029 / 3.8

No recurrence

0.8000.17

Nodal recurrence

0.9000.04 0.0000

Brain mets

0.0800.00 0.0000

Bone m ets

0.0200.00 0.0000

Nodal/brain/bone recurrence

0.2000.000 / 18.4

Non head and neck

0.7900.000 / 3.7

Node negative

0.7600.006 / 3.7

Stage II-III

0.4400.034 / 7.1

Non desm oplastic

0.9800.015 / 3.3

Stage I-III

0.8500.033 / 3.7

Nodal recurrence

0.9000.03 0.0000

Brain mets

0.0800.00 0.0000

Bone m ets

0.0200.00 0.0000

Sy mptomatic brain/bone/node metastases

0.5100.000 / 13.9

No sy mptom atic brain/bone/node m etastases

0.4900.03

Stage IV

0.0700.000 / 7.1

Cutaneous

0.9700.033 / 3.7

Melanom a 0.035 / 3.9

Total Proportion 5-yr benefi t proportion

Indication Number of Fractions

Local control

Local control/fractions

Proportion of all cases

Mucosal Melanoma

30 25% 0.83% 1%

Lentigo 10 98% 9.8% 1%

Desmoplastic 20 91% 4.55 2%

LN +ve 20 14% 0.7% 7%

H&N pT4 20 18% 0.9% 1%

All cases 3.9 3.5% 0.90% 100%

Estimating cost and benefit Melanoma

GTFRCC

Adapting model to other jurisdictiosn

• Cancer data

• Stage data

• Available models

– RTU

– Survival

• Cost data

Effect of tumour distribution

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

East

Africa

Central

Africa

Northern

Africa

Southern

Africa

Western

Africa

all Africa

Liver

Ovary etc.

Leukaemia

Thyroid

Testis

Multiple myeloma

Melanoma of skin

Kidney etc.

Corpus uteri

Other Pharynx

Pancreas

Hodgkin's disease

Colon/Rectum

Brain, nervous system

Nasopharynx

Larynx

Oral cavity

Lung

Stomach

Bladder

Non-Hodgkin lymphoma

Oesophagus

Prostate

Cervix uteri

Breast

0%

10%

20%

30%

40%

50%

60%

70%

Central Africa WesternAfrica

East Africa SouthernAfrica

NorthernAfrica

RTU

Globocan data sources

High quality national or

regional (coverage >50%)

7%

High quality regional

(coverage 10% to 50%) 11% High quality

regional (coverage <10%)

42%

National data 5%

Regional data 16%

Frequency data 8%

No data 11%

Data quality and GNI/person

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

A B C D E F G

Ave

rage

GN

I/p

ers

on

Data Quality

A. High quality national or

regional (coverage

>50%) 7%

B. High quality regional

(coverage 10% to 50%)

11%

C. High quality regional

(coverage <10%) 42%

D. National data 5%

E. Regional data 16%

F. Frequency data 8%

G. No data 11%

Top 10 cancers

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Pro

po

rtio

n o

f al

l can

cers

Less developed

Medium development

High development

Very High development

Individual country variation

0%

5%

10%

15%

20%

25%

30%

35%

Swit

zerl

and

New

Zea

lan

d

Egyp

t

Au

stri

a

Luxe

mb

ou

rg

Gre

ece

Spai

n

Sau

di A

rab

ia

Ko

rea,

Rep

ub

lic o

f

Cze

ch R

epu

blic

Pu

erto

Ric

o

Arm

enia

No

rth

ern

Afr

ica

Po

lyn

esia

Cu

ba

Geo

rgia

Bo

snia

He

rzeg

ovi

na

Aze

rbai

jan

Asi

a

Tim

or-

Lest

e

Turk

men

ista

n

Mal

div

es

Trin

idad

an

d T

ob

ago

Ph

ilip

pin

es

Uzb

eki

stan

El S

alva

do

r

Nam

ibia

Ch

ad

Kyr

gyzs

tan

Eth

iop

ia

Me

lan

esia

Som

alia

Uga

nd

a

Fiji

Be

nin

Rw

and

a

Lib

eria

Mal

i

Tan

zan

ia

Swaz

ilan

d

Pro

po

rtio

n o

f al

l can

cers

Cervix

Available models

RTU Stage data Survival model

Lung 77% y y Breast 87% y y Stomach 27% y y Liver 0% n/a y Colorectum 22% ? y Haematology 40% Y y Cervix uteri 71% y y Oesophagus 71% y Head and Neck 74% y y Prostate 58% y n