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Validation of the CancerMath prognostic tool for breast cancer in Southeast Asia

  • Hui Miao1Email author,
  • Mikael Hartman1, 2, 3,
  • Helena M. Verkooijen4,
  • Nur Aishah Taib5,
  • Hoong-Seam Wong6,
  • Shridevi Subramaniam6,
  • Cheng-Har Yip5,
  • Ern-Yu Tan7,
  • Patrick Chan7,
  • Soo-Chin Lee8 and
  • Nirmala Bhoo-Pathy6, 9, 10
BMC Cancer201616:820

https://doi.org/10.1186/s12885-016-2841-9

Received: 16 December 2015

Accepted: 5 October 2016

Published: 21 October 2016

Abstract

Background

CancerMath is a set of web-based prognostic tools which predict nodal status and survival up to 15 years after diagnosis of breast cancer. This study validated its performance in a Southeast Asian setting.

Methods

Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).

Results

The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %–44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70–0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75–0.79) and 0.74 (95 % CI: 0.71–0.76).

Conclusions

The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.

Keywords

Breast cancer CancerMath Prognostic model Asia

Background

Adjuvant chemotherapy and hormone therapy improve long-term survival and reduce the risk of recurrence in early breast cancer patients [13]. However, the benefit varies greatly from patient to patient due to biologic heterogeneity of the disease and differences in response to treatment [4, 5]. Risk of adverse effects and high cost of adjuvant therapy also make it challenging for oncologists to choose the most appropriate treatment. Therefore, several clinical tools have been developed to predict prognosis and survival benefit from treatment, using clinicopathological features, genetic profiles, and novel biomarkers [6].

The Nottingham Prognostic Index was the first prognostic model introduced for breast cancer patients in 1982. It includes only tumor grade, size, and nodal status for prediction of disease-free survival [7, 8]. The widely used Adjuvant! Online (www.adjuvantonline.com) calculates 10-year overall survival and disease-free survival of patients with non-metastatic breast cancer, based on patient’s age, tumor size, grade, estrogen-receptor (ER) status, nodal status, and co-morbidities. It also quantitatively predicts the absolute gain from adjuvant therapy [9]. Although it is recommended by the National Institute for Health and Clinical Excellence and widely used by oncologists [1013], several validation studies have suggested that Adjuvant! Online is suboptimal in women younger than 40 years and older than 75 years [14, 15]. The model was recently validated in Malaysia, Korea, and Taiwan, where it was shown to substantially overestimate actual survival [1618]. CancerMath (http://www.lifemath.net/cancer/) is the latest web-based prognostic tool, which takes human epidermal growth factor receptor 2 (HER2) status into account [19]. It was established based on the binary biological model of cancer metastasis and the parameters were derived from the Surveillance, Epidemiology and End-Result (SEER) registry in the United States [20]. CancerMath provides information on overall survival, conditional survival (the likelihood of surviving given being alive after a certain number of years) and benefit of systemic treatment for each of the first 15 years after diagnosis. This model also estimates probability of positive lymph nodes and nipple involvement. Validation study has shown comparable results between CancerMath and Adjuvant! Online [19]. However this new tool has not been validated outside the United States. Given the differences in underlying distribution of prognostic factors and life expectancy between Asia and the United States [2123], direct application without any correction may not generate reliable prediction. The aim of the study is to validate this model in the Singapore Malaysia Hospital-Based Breast Cancer Registry, demonstrating its predictive performance for different subgroups and determining its calibration and discrimination.

Methods

Women diagnosed with pathological stage I to III breast cancer according to American Joint Committee on Cancer Staging Manual sixth edition, who underwent surgery, were identified from the Singapore Malaysia Hospital-Based Breast Cancer Registry, which combined databases from three public tertiary hospitals. The breast cancer registry at National University Hospital (NUH) in Singapore collects information on breast cancer patients diagnosed since 1990. The Tan Tock Seng Hospital (TTSH) registry registers patients diagnosed from 2001 onwards. The University Malaya Medical Centre (UMMC), located in Kuala Lumpur, Malaysia, has prospectively collected data on breast cancer patients diagnosed since 1993 [24]. No consent was needed and ethics approval was obtained from Domain Specific Review Board under National Healthcare Group in Singapore and Medical Ethics Committee under UMMC. The consolidated registry included information on ethnicity, age and date of diagnosis, histologically determined tumor size, number of positive lymph nodes, ER and progesterone receptor (PR) status (positive defined as 1 % or more positively stained tumor cells at NUH or 10 % or more positively stained tumor cells at TTSH and UMMC, negative, or unknown), HER2 status based on fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC) if FISH was not performed (positive defined as FISH positive or IHC score of 3+, negative defined as FISH negative or IHC scored of 0 or 1+, equivocal defined as IHC score of 2+, or unknown), histological type (ductal, lobular, mucinous, others, or unknown), grade (1, 2, 3, or unknown), type of surgery (no surgery, mastectomy, breast conserving surgery, or unknown), chemotherapy (yes, no or unknown), hormone therapy (yes, no, or unknown), and radiotherapy (yes, no, or unknown). Detailed chemotherapeutic treatment regimens were only available for UMMC patients. For chemotherapy, cyclophosphamide, methotrexate and fluorouracil (CMF) was categorized as first generation regimen and fluorouracil, epirubicin and cyclophosphamide (FEC), and doxorubicin and cyclophosphamide (AC) followed by paclitaxel were second generation. Docetaxel, doxorubicin and cyclophosphamide (TAC), and FEC followed by docetaxel were categorized as third generation. Hormone therapy was categorized into five groups: tamoxifen, aromatase inhibitors (AI), tamoxifen followed by AI, ovarian ablation, and ovarian ablation plus tamoxifen. Vital status was obtained from the hospitals' medical records and ascertained by linkage to death registries in both countries. Patients diagnosed until 31st December 2011 were followed up from date of diagnosis until date of death or date of last fellow-up, whichever came first. Date of last follow-up was 1st March 2013 for UMMC, 31st July 2013 for NUH, and 1st October 2012 for TTSH. Male patients, patients with unknown age at diagnosis and tumor size were excluded from this analysis as these two were essential predictors for all four CancerMath calculators.

Javascript codes of all four CancerMath calculators which contained predetermined parameters and mathematical equations were exported on 9th Nov 2013 from its website by selecting “view- > source” in the browser menu. The script was then transcribed into R script to allow calculation for a group of patients. For nodal status calculator, patient’s age, tumor size, ER and PR status, histological type, and grade were used by the program to calculate probability of positive nodes for each patient. Overall mortality risk at each year up to 15 year after diagnoses was predicted by outcome calculator, based on age, tumor size, number of positive nodes, grade, histological type, ER, PR, and HER2 status. Effect of hormone and chemotherapeutic regimen on overall mortality was further adjusted by the therapy calculator and number of years since diagnosis were considered in the conditional survival calculator. Results from R script and website were crosschecked with a random subset of 20 patients to verify the accuracy of R script. Histological type recorded as others was re-categorized as unknown. If HER2 status was equivocal based on IHC and FISH was not performed, HER2 status was treated as unknown. Evidence of recurrence was set as unknown for conditional survival calculation.

In total, 7064 female breast cancer patients were included. Only cases with known nodal status (N = 6807) were included for validation of nodal status calculator and their individual probability of positive lymph nodes was calculated. For outcome calculator, two separate subsets of patients with minimum 5-year follow up (UMMC and NUH patients diagnosed in 2007 and earlier and TTSH patient diagnosed in 2006 and earlier, N = 4517) and patients with 10-year follow-up UMMC and NUH cases diagnosed in 2002 and earlier, N = 1649) were selected for comparison of observed and predicted survival. As NUH and TTSH did not collect details of hormone therapy and chemotherapy regimen data before 2006, therapy calculator was only validated for UMMC patients with minimum 5-year follow up (N = 1538).

Statistical analysis

Nodal status calculator

Observed and predicted probability of positive lymph nodes were compared. Calibration was assessed by dividing the data into deciles based on the predicted probability of positive nodes and then plotting the observed probability of positive nodes against means of predicted probability for each decile. A 45 degree diagonal line was plotted to illustrate perfect agreement. Discrimination of nodal status calculator was evaluated by area under the curve (AUC) in receiver operating characteristic analysis. A value of 0.5 indicates no discrimination and a value of 1.0 means perfect discrimination.

Outcome and therapy calculator

Ratio of observed and predicted numbers of death within 5 years and 10 years of diagnosis were calculated as mortality ratio (MR) with 95 % confidence interval (CI) constructed by exact procedure [25]. MR was also calculated for different subgroups by country, period of diagnosis, age, race, and other clinical characteristics. Observed 5-year and 10-year survival rates were compared with the median predicted survival from CancerMath. A difference of less than 3 % would be considered reliable enough for clinical use as 10-year survival benefit of 3–5 % is an indication for adjuvant chemotherapy [26]. The relationship of average 5-year and 10-year predicted survival and observed 5-year and 10-year survival was illustrated by the calibration plot. Discrimination of outcome and therapy calculator was evaluated by AUC using dataset with minimum 5-year and 10-year follow-up accordingly. Outcome calculator was further evaluated using concordance index (c-index) proposed by Harrell et al. for the entire dataset regardless of follow-up time [27]. C-index is the probability of correctly distinguishing patient who survives longer within a random pair of patients [27]. Like for the AUC, a c-index of 0.5 indicates no discrimination and a c-index of 1.0 means perfect discrimination.

Conditional survival calculator

For patients who survived two years after diagnosis, predicted 5-year survival was compared with observed 5-year survival. Similarly predicted 10-year survival was compared with observed 10-year survival for patients who survived 5 years and 7 years respectively. Discrimination was evaluated by AUC.

Results

In total, 7064 female breast cancer patients were included. Tables 1, 2, 3 and 4 present clinical characteristics of 6807 patients with nodal status, 4517 patients with minimum 5-year follow-up, 1649 patients with 10-year follow-up, and 1538 patients with detailed treatment data and minimum of 5-years follow-up, respectively.
Table 1

Observed number of patients with positive lymph nodes and predicted probability of positive nodes

 

Number of patients

Number of patients with positive lymph nodes (percentage)

Predicted probability of positive nodes (median)

Overall

6807

2970 (43.6 %)

40.6 %

Ethnicity

 Chinese

5029

2062 (41.0 %)

39.2 %

 Malay

963

511 (53.1 %)

46.0 %

 Indian

651

312 (47.9 %)

44.7 %

 Other

164

85 (51.8 %)

39.5 %

Country

 Malaysia

3274

1460 (44.6 %)

43.0 %

 Singapore

3533

1510 (42.7 %)

38.5 %

Period of diagnosis

 1990–1994

124

58 (46.8 %)

52.0 %

 1995–1999

547

258 (47.2 %)

41.9 %

 2000–2003

1744

755 (43.3 %)

41.4 %

 2004–2007

2129

964 (45.3 %)

41.2 %

 2008–2011

2263

935 (41.3 %)

38.9 %

Age at diagnosis

 0–39

670

310 (46.3 %)

47.1 %

 40–49

2039

910 (44.6 %)

42.9 %

 50–59

2145

934 (43.5 %)

41.4 %

 60–69

1301

546 (42.0 %)

36.7 %

 70+

652

270 (41.4 %)

34.3 %

Tumor size (mm)

 0–20

2926

822 (28.1 %)

26.4 %

 21–50

3247

1678 (51.7 %)

49.3 %

 51+

634

470 (74.1 %)

79.2 %

ER status

 Negative

2316

1037 (44.8 %)

43.5 %

 Positive

4254

1854 (43.6 %)

38.5 %

 Unknown

237

79 (33.3 %)

44.5 %

PR status

 Negative

2656

1195 (45.0 %)

42.1 %

 Positive

3507

1511 (43.1 %)

38.5 %

 Unknown

644

264 (41.0 %)

44.2 %

Her2 status

 Negative

2872

1197 (41.7 %)

39.2 %

 Equivocal

429

182 (42.4 %)

39.2 %

 Positive

1315

662 (50.3 %)

45.0 %

 Unknown

2191

929 (42.4 %)

39.6 %

Histology

 Ductal

5945

2681 (45.1 %)

41.5 %

 Lobular

287

150 (52.3 %)

37.9 %

 Mucinous

219

34 (15.5 %)

10.7 %

 Others

352

102 (29.0 %)

35.8 %

 Unknown

4

3 (75.0 %)

25.1 %

Grade

 1

849

204 (24.0 %)

21.8 %

 2

2836

1278 (45.1 %)

40.6 %

 3

2463

1275 (51.8 %)

46.4 %

 Unknown

659

213 (32.3 %)

35.9 %

Table 2

Observed and predicted 5-year overall survival from outcome calculator, stratified by patients’ characteristics

 

N

Observed deaths in 5 years

Predicted deaths in 5 years

Mortality Ratio (95 % CI)

Observed 5-year survival (%) (std err)

Predicted 5-year survival (median) (%)

Absolute difference (%) (95 % CI)

Overall

4517

752

667

1.13(1.05,1.21)

83.4 (0.006)

87.3

3.9 (2.7,5.1)

 Ethnicity

 Chinese

3340

488

478

1.02(0.93,1.12)

85.4 (0.006)

88.0

2.6 (1.4,3.8)

 Malay

654

143

104

1.38(1.16,1.62)

78.1 (0.016)

85.8

7.7 (4.6,10.8)

 Indian

430

109

71

1.54(1.26,1.85)

74.7 (0.021)

85.1

10.4 (6.3,14.5)

 Other

93

12

14

0.86(0.44,1.50)

87.1 (0.035)

87.3

0.2 (−6.7,7.1)

Country

 Malaysia

2143

423

331

1.28(1.16,1.41)

80.3 (0.009)

86.1

5.8(4.0,7.6)

 Singapore

2374

329

336

0.98(0.88,1.09)

86.1 (0.007)

88.6

2.5(1.1,3.9)

Period of diagnosis

 1990–1994

140

41

22

1.86(1.34,2.53)

70.7 (0.038)

85.9

15.2 (7.8,22.6)

 1995–1999

564

116

75

1.55(1.28,1.86)

79.8 (0.017)

87.9

8.1 (4.8,11.4)

 2000–2003

1800

279

261

1.07(0.95,1.20)

84.5 (0.009)

87.8

3.3 (1.5,5.1)

 2004–2007

2013

316

309

1.02(0.91,1.14)

84.3 (0.008)

87.2

2.9 (1.3,4.5)

Age at diagnosis

 0–39

493

101

64

1.58(1.29,1.92)

79.5 (0.018)

88.8

9.3(5.8,12.8)

 40–49

1430

172

163

1.06(0.90,1.23)

88.0 (0.009)

90.6

2.6(0.8,4.4)

 50–59

1412

224

194

1.15(1.01,1.32)

84.1 (0.010)

88.2

4.1(2.1,6.1)

 60–69

776

126

130

0.97(0.81,1.15)

83.8 (0.013)

85.1

1.3(−1.2,3.8)

 70+

406

129

117

1.10(0.92,1.31)

68.2 (0.023)

73.9

5.7 (1.2,10.2)

Tumor size (mm)

 0–20

1889

151

173

0.87(0.74,1.02)

92.0 (0.006)

92.9

0.9(−0.3,2.1)

 21–50

2180

438

374

1.17(1.06,1.29)

79.9 (0.009)

84.8

4.9 (3.1,6.7)

 51+

448

163

121

1.35(1.15,1.57)

63.6 (0.023)

73.6

10.0(5.5,14.5)

Number of positive nodes

 0

2408

196

238

0.82(0.71,0.95)

91.9 (0.006)

91.7

−0.2(−1.4,1.0)

 1–3

1068

195

165

1.18(1.02,1.36)

81.7 (0.012)

85.9

4.2(1.8,6.6)

 4–9

533

159

122

1.30(1.11,1.52)

70.2 (0.020)

78.0

7.8(3.9,11.7)

 10+

354

170

116

1.47(1.25,1.70)

52.0 (0.027)

67.4

15.4(10.1,20.7)

 Unknown

154

32

27

1.19(0.81,1.67)

79.2 (0.033)

86.6

7.4(0.9,13.9)

ER status

 Negative

1595

392

268

1.46(1.32,1.61)

75.4 (0.011)

85.2

9.8 (7.6,12.0)

 Positive

2668

309

367

0.84(0.75,0.94)

88.4 (0.006)

88.8

0.4(−0.8,1.6)

 Unknown

254

51

33

1.55(1.15,2.03)

79.9 (0.025)

88.6

8.7(3.8,13.6)

PR status

 Negative

1674

382

289

1.32(1.19,1.46)

77.2 (0.010)

84.8

7.6(5.6,9.6)

 Positive

2174

241

285

0.85(0.74,0.96)

88.9 (0.007)

89.5

0.6(−0.8,2.0)

 Unknown

669

129

93

1.39(1.16,1.65)

80.7 (0.015)

87.4

6.7 (3.8,9.6)

Her2 status

 Negative

1483

208

210

0.99(0.86,1.13)

86.0 (0.009)

88.0

2.0(0.2,3.8)

 Equivocal

118

19

19

1.00(0.60,1.56)

83.9 (0.034)

87.4

3.5(−3.2,10.2)

 Positive

790

172

147

1.17(1.00,1.36)

78.2 (0.015)

83.0

4.8(1.9,7.7)

 Unknown

2126

353

292

1.21(1.09,1.34)

83.4 (0.008)

88.7

5.3(3.7,6.9)

Histology

 Ductal

3951

696

597

1.17(1.08,1.26)

82.4 (0.006)

87.0

4.6 (3.4,5.8)

 Lobular

180

17

26

0.65(0.38,1.05)

90.6 (0.022)

87.5

−3.1(−7.4,1.2)

 Mucinous

156

10

14

0.71(0.34,1.31)

93.6 (0.020)

94.7

1.1(−2.8,5.0)

 Others

227

29

30

0.97(0.65,1.39)

87.2 (0.022)

89.5

2.3(−2.0,6.6)

 Unknown

3

0

0

-

100

86.8

−13.2

Grade

 1

552

20

44

0.45(0.28,0.70)

96.4 (0.008)

94.7

−1.7(−3.3,–0.1)

 2

1882

261

265

0.98(0.87,1.11)

86.1 (0.008)

88.2

2.1(0.5,3.7)

 3

1591

402

288

1.40(1.26,1.54)

74.7 (0.011)

84.3

9.6(7.4,11.8)

 Unknown

492

69

70

0.99(0.77,1.25)

86.0 (0.016)

87.4

1.4(−1.7,4.5)

Numbers marked in bold indicate statistically significant difference at the 95% confidence level

Table 3

Observed and predicted 10-year overall survival from outcome calculator, stratified by patients’ characteristics

 

N

Observed death in 10 years

Predicted death in 10 years

Mortality Ratio (95 % CI)

Observed 10-year survival (%)(std err)

Predicted 10-year survival (median) (%)

Absolute difference (%) (95 % CI)

Overall

1649

488

454

1.07(0.98,1.17)

70.4 (0.011)

75.3

4.9 (2.7,7.1)

 Ethnicity

 Chinese

1201

318

318

1.00(0.89,1.12)

73.5 (0.013)

76.8

3.3 (0.8,5.8)

 Malay

251

100

74

1.35(1.10,1.64)

60.2 (0.031)

72.3

12.1(6.0,18.2)

 Indian

174

64

55

1.16(0.90,1.49)

63.2 (0.037)

69.9

6.7 (−0.6,14.0)

 Other

23

6

7

0.86(0.31,1.87)

73.9 (0.092)

77.1

3.2 (−14.8,21.2)

Country

 Malaysia

983

341

284

1.20(1.08,1.34)

65.3 (0.015)

73.3

8.0 (5.1,10.9)

 Singapore

666

147

170

0.86(0.73,1.02)

77.9 (0.016)

77.9

0.0 (−3.1,3.1)

Period of diagnosis

 1990–1994

140

56

42

1.33(1.01,1.73)

60.0 (0.041)

72.5

12.5(4.5,20.5)

 1995–1999

564

187

148

1.26(1.09,1.46)

66.8 (0.020)

76.0

9.2 (5.3,13.1)

 2000–2002

945

245

264

0.93(0.82,1.05)

74.1 (0.014)

75.9

1.8 (−0.9,4.5)

Age at diagnosis

 0–39

232

82

58

1.41(1.12,1.75)

64.7 (0.031)

77.3

12.6 (6.5,18.7)

 40–49

576

137

130

1.05(0.88,1.25)

76.2 (0.018)

80.2

4.0 (0.5,7.5)

 50–59

493

141

129

1.09(0.92,1.29)

71.4 (0.020)

76.4

5.0 (1.1,8.9)

 60–69

254

78

86

0.91(0.72,1.13)

69.3 (0.029)

68.4

−0.9 (−6.6,4.8)

 70+

94

50

50

1.00(0.74,1.32)

46.8 (0.051)

50.1

3.3 (−6.7,13.3)

Tumor size (mm)

 0–20

653

118

109

1.08(0.90,1.30)

81.9 (0.015)

86.8

4.9 (2.0,7.8)

 21–50

831

283

262

1.08(0.96,1.21)

65.9 (0.016)

70.6

4.7 (1.6,7.8)

 51+

165

87

82

1.06(0.85,1.31)

47.3 (0.039)

50.6

3.3 (–4.3,10.9)

Number of positive nodes

 0

867

147

161

0.91(0.77,1.07)

83.0 (0.013)

84.0

1.0 (−1.5,3.5)

 1–3

407

143

120

1.19(1.00,1.40)

64.9 (0.024)

72.1

7.2 (2.5,11.9)

 4–9

215

112

93

1.20(0.99,1.45)

47.9 (0.034)

58.2

10.3 (3.6,17.0)

 10+

104

71

62

1.15(0.89,1.44)

31.7 (0.046)

39.9

8.2 (−0.8,17.2)

 Unknown

56

15

17

0.88(0.49,1.46)

73.2 (0.059)

73.5

0.3 (−11.3,11.9)

ER status

 Negative

637

224

197

1.14(0.99,1.30)

64.8 (0.019)

71.5

6.7 (3.0,10.4)

 Positive

816

205

206

1.00(0.86,1.14)

74.9 (0.015)

78.2

3.3 (0.4,6.2)

 Unknown

196

59

51

1.16(0.88,1.49)

69.9 (0.033)

76.8

6.9 (0.4,13.4)

PR status

 Negative

485

160

153

1.05(0.89,1.22)

67.0 (0.021)

70.7

3.7(−0.4,7.8)

 Positive

564

128

136

0.94(0.79,1.12)

77.3 (0.018)

79.9

2.6 (−0.9,6.1)

 Unknown

600

200

165

1.21(1.05,1.39)

66.7 (0.019)

74.1

7.4 (3.7,11.1)

Her2 status

 Negative

269

72

66

1.09(0.85,1.37)

73.2 (0.027)

78.3

5.1(−0.2,10.4)

 Equivocal

13

6

4

1.50(0.55,3.26)

53.8 (0.138)

65.5

11.7 (−15.3,38.7)

 Positive

335

113

110

1.03(0.85,1.24)

66.3 (0.026)

69.1

2.8 (−2.3,7.9)

 Unknown

1032

297

273

1.09(0.97,1.22)

71.2 (0.014)

76.8

5.6 (2.9,8.3)

Histology

 Ductal

1418

445

401

1.11(1.01,1.22)

68.6 (0.012)

74.4

5.8 (3.4,8.2)

 Lobular

78

18

21

0.86(0.51,1.35)

76.9 (0.048)

75.7

−1.2 (−10.6,8.2)

 Mucinous

59

9

9

1.00(0.46,1.90)

84.7 (0.047)

91.2

6.5 (−2.7,15.7)

 Others

91

16

22

0.73(0.42,1.18)

82.4 (0.040)

77.7

−4.7 (−12.5,3.1)

 Unknown

3

0

1

-

100

74.4

−25.6

Grade

 1

200

22

31

0.71(0.44,1.07)

89.0 (0.022)

89.3

0.3 (−4.0,4.6)

 2

668

188

176

1.07(0.92,1.23)

71.9 (0.017)

77.1

5.2 (1.9, 8.5)

 3

510

196

172

1.14(0.99,1.31)

61.6 (0.022)

70.0

8.4 (4.1,12.7)

 Unknown

271

82

76

1.08(0.86,1.34)

69.7 (0.028)

73.3

3.6 (−1.9,9.1)

Numbers marked in bold indicate statistically significant difference at the 95% confidence level

Table 4

Observed and predicted 5-year overall survival from therapy calculator, stratified by patients’ characteristics

 

N

Observed death in 5 years

Predicted death in 5 years

Mortality Ratio (95 % CI)

Observed 5-year survival (%)(std err)

Predicted 5-year survival (median) (%)

Absolute difference (%) (95 % CI)

Overall

1538

286

173

1.65(1.47,1.86)

81.4 (0.010)

89.8

8.4(6.4,10.4)

Ethnicity

 Chinese

1052

167

113

1.48(1.26,1.72)

84.1 (0.011)

90.4

6.3(4.1,8.5)

 Malay

264

62

30

2.07(1.58,2.65)

76.5 (0.026)

89.4

12.9(7.8,18.0)

 Indian

212

54

29

1.86(1.40,2.43)

74.5 (0.030)

87.2

12.7(6.8,18.6)

 Other

10

3

1

3.00(0.62,8.77)

70.0 (0.145)

88.2

18.2(−10.2,46.6)

Period of diagnosis

 1990–1994

95

39

14

2.79(1.98,3.81)

58.9 (0.05)

86.8

27.9 (18.1,37.7)

 1995–1999

374

93

40

2.33(1.88,2.85)

75.1 (0.022)

90.9

15.8 (11.5,20.1)

 2000–2003

568

91

63

1.44(1.16,1.77)

84.0 (0.015)

89.7

5.7 (2.8,8.6)

 2004–2007

501

63

56

1.13(0.86,1.44)

87.4 (0.015)

90.2

2.8 (−0.1,5.7)

Age at diagnosis

 0–39

205

55

17

3.24(2.44,4.21)

73.2 (0.031)

92.6

19.4(13.3,25.5)

 40–49

515

74

41

1.80(1.42,2.27)

85.6 (0.015)

92.9

7.3 (4.4,10.2)

 50–59

449

86

50

1.72(1.38,2.12)

80.8 (0.019)

89.4

8.6 (4.9,12.3)

 60–69

271

43

40

1.08(0.78,1.45)

84.1 (0.022)

86.1

2.0 (−2.3,6.3)

 70+

98

28

24

1.17(0.78,1.69)

71.4 (0.046)

77.4

6.0 (−3.0,15.0)

Tumor size (mm)

 0–20

547

51

39

1.31(0.97,1.72)

90.7 (0.012)

94.2

3.5 (1.1,5.9)

 21–50

813

170

102

1.67(1.43,1.94)

79.1 (0.014)

88.5

9.4 (6.7,12.1)

 51+

178

65

32

2.03(1.57,2.59)

63.5 (0.036)

82.8

19.3 (12.2,26.4)

Number of positive nodes

 0

806

72

70

1.03(0.80,1.30)

91.1 (0.010)

92.4

1.3(−0.7,3.3)

 1–3

389

83

46

1.80(1.44,2.24)

78.7 (0.021)

89.4

10.7(6.6,14.8)

 4–9

192

64

30

2.13(1.64,2.72)

66.7 (0.034)

85.8

19.1(12.4,25.8)

 10+

123

61

23

2.65(2.03,3.41)

50.4 (0.045)

82.3

31.9(23.1,40.7)

 Unknown

28

6

4

1.50(0.55,3.26)

78.6 (0.078)

90.6

12.0 (−3.3,27.3)

ER status

 Negative

528

146

73

2.00(1.69,2.35)

72.3 (0.019)

87.2

14.9(11.2,18.6)

 Positive

850

99

82

1.21(0.98,1.47)

88.4 (0.011)

91.7

3.3 (1.1,5.5)

 Unknown

160

41

18

2.28(1.63,3.09)

74.4 (0.035)

89.8

15.4 (8.5,22.3)

PR status

 Negative

423

106

57

1.86(1.52,2.25)

74.9 (0.021)

87.4

12.5(8.4,16.6)

 Positive

586

73

58

1.26(0.99,1.58)

87.5 (0.014)

91.6

4.1 (1.4,6.8)

 Unknown

529

107

58

1.84(1.51,2.23)

79.8 (0.017)

90.2

10.4 (7.1,13.7)

Her2 status

 Negative

665

78

68

1.15(0.91,1.43)

88.3 (0.012)

91.1

2.8 (0.4,5.2)

 Equivocal

35

7

4

1.75(0.70,3.61)

80.0 (0.068)

89.9

9.9 (−3.4,23.2)

 Positive

418

84

53

1.58(1.26,1.96)

79.9 (0.020)

87.9

8.0 (4.1,11.9)

 Unknown

420

117

48

2.44(2.02,2.92)

72.1 (0.022)

89.7

17.6 (13.3,21.9)

Histology

 Ductal

1346

270

155

1.74(1.54,1.96)

79.9 (0.011)

89.6

9.7 (7.5,11.9)

 Lobular

71

7

7

1.00(0.40,2.06)

90.1 (0.035)

91.0

0.9 (−6.0,7.8)

 Mucinous

58

1

4

0.25(0.01,1.39)

98.3 (0.017)

96.0

−2.3 (−5.6,1.0)

 Others

63

8

7

1.14(0.49,2.25)

88.9 (0.040)

89.7

0.8 (−7.0,8.6)

Grade

 1

161

8

11

0.73(0.31,1.43)

95.0 (0.017)

95.6

0.6 (−2.7,3.9)

 2

661

111

71

1.56(1.29,1.88)

83.2 (0.015)

90.5

7.3 (4.4,10.2)

 3

433

119

59

2.02(1.67,2.41)

72.5 (0.021)

87.7

15.2 (11.1,19.3)

 Unknown

283

48

32

1.50(1.11,1.99)

83.0 (0.022)

89.8

6.8 (2.5,11.1)

Chemo-therapy

No chemo-therapy

440

58

53

1.09(0.83,1.41)

86.8 (0.016)

90.4

3.6 (0.5,6.7)

 1st Gen

162

49

21

2.33(1.73,3.08)

69.8 (0.036)

88.1

18.3 (11.2,25.4)

 2nd Gen

915

174

97

1.79(1.54,2.08)

81.0 (0.013)

90.0

9.0 (6.5,11.5)

 3rd Gen

21

5

2

2.50(0.81,5.83)

76.2 (0.093)

90.8

14.6 (−3.6,32.8)

Hormone-therapy

 No

398

108

51

2.12(1.74,2.56)

72.9 (0.022)

87.7

14.8 (10.5,19.1)

 Yes

1140

178

122

1.46(1.25,1.69)

84.4 (0.011)

90.8

6.4 (4.2,8.6)

Numbers marked in bold indicate statistically significant difference at the 95% confidence level

Nodal status calculator

A total of 6807 patients with nodal status data were selected for validation of nodal status calculator. In this dataset, 43.6 % patients (n = 2970) (95 % CI, 42.5 %–44.8 %) had at least one positive lymph node and the median predicted probability was 40.6 %. CancerMath underestimated the probability of positive node for most of the subgroups (Table 1). The calibration plot (Fig. 1) also illustrated underestimation except for the last two deciles. The discrimination of this calculator was fair, with AUC of 0.71 (95 % CI, 0.70–0.72).
Fig. 1

Calibration plot of observed probability of positive nodes with 95 % confidence interval against predicted probability of positive nodes (mean) by deciles of the predicted value

Outcome calculator

The observed number of deaths within 5 years after diagnosis was significantly higher than the predicted number of deaths (752 vs 667, MR = 1.13, 95 % CI 1.05–1.21). The number of observed and predicted number of deaths within 10 years after diagnosis was not significant (488 vs 454, MR = 1.07, 95 % CI 0.98–1.17). The absolute differences of 5-year and 10-year predicted and observed survival probabilities were 3.9 % and 4.9 %. Overestimation was more pronounced in Malaysian patients than in Singaporean patients (5.8 % vs 2.5 % for 5-year survival, and 8.0 % vs 0.0 % for 10-year survival). We also observed notable differences for cases diagnosed in earlier period and of younger age (Tables 2 and 3). In addition, CancerMath significantly overpredicted survival for patients with unfavorable prognostic characteristics such as large tumor size, more positive nodes and ER negative tumor. For those with relatively better predicted survival, CancerMath predictions were similar to observed outcome (Fig. 2a, b and c). The difference between 5-year predicted and observed survival was 15 %, 3 % and 1 % for the first, fifth, and tenth deciles respectively. The Kaplan-Meier curves of overall survival by quintiles of predicted 5-year survival were illustrated in Fig. 3. The difference in survival experience between the five groups was statistically significant (p-value < 0.001 by the log-rank test). The AUC for 5-year and 10-year overall survival were 0.77 (95 % CI,0.75–0.79) and 0.74 (95 % CI,0.71–0.76), respectively whereas the c-index was 0.74 (95 % CI, 0.72–0.75). Both measures demonstrated fair discrimination.
Fig. 2

Calibration plot of observed survival with 95 % confidence interval against predicted survival (mean) by deciles of the predicted value. a 5-year survival from outcome calculator for Malaysian patients, b 5-year survival from outcome calculator for Singaporean patients, c 10-year survival from outcome calculator, d 5-year survival from therapy calculator

Fig. 3

Kaplan-Meier curves of overall survival by quintiles of 5-year predicted survival from outcome calculator

Therapy calculator

For therapy calculator which was only validated in Malaysian patients, predicted survival was significantly higher than the observed survival for almost all subgroups, except for those diagnosed recently and with more favorable tumor characteristics (Table 4, Fig. 2d). The calculator showed fair discrimination at 5-year overall survival (AUC = 0.73, 95 % CI 0.70–0.77).

Conditional survival calculator

For patients who have survived 2 years since diagnosis, the predicted 5-year survival was 91.0 % versus the observed survival of 88.3 %. The AUC was 0.75 (95 % CI, 0.73–0.77). For patients who have survived 5 years and 7 years, the predicted probability of surviving up to 10 years was 86.6 % and 91.7 %. Whereas the observed survival was 85.3 % and 91.0 % correspondingly. The AUC was 0.66 (95 % CI, 0.62–0.70) and 0.63 (95 % CI, 0.57–0.68) for 10-year survival.

Discussion

Many prognostic tools have been developed over the past two decades to aid clinical decision making for breast cancer patients. This study validated four different prognostic calculators provided by CancerMath in the Singapore-Malaysia Hospital-Based Breast Cancer Registry. The discrimination was fair for nodal status calculator. CancerMath outcome, therapy and conditional survival calculator also moderately discriminated between survivors and non-survivors at 5 years and 10 years after diagnosis. It however consistently overestimated survival for this cohort of Southeast Asian patients, especially for those with poor prognostic profile.

CancerMath was previously built and validated using SEER data and patients diagnosed at Massachusetts General and Brigham and Women’s Hospitals [19]. In the SEER database, 82.7 % of the invasive breast cancer cases diagnosed between 2003 and 2007 were white and only 6.9 % were Asian/ /Pacific Islander [28]. It was shown that the differences between observed and predicted survival was within 2 % for 97 % of the patients in the validation set [19]. Our study is the first one to independently validate CancerMath outside United States and is also the largest validation study of a western-derived breast cancer prognostic model in Asia. We demonstrated that CancerMath overpredicted survival by more than 3 % for almost all clinical and pathological subgroups. The findings were similar to previous validation studies of Adjuvant! Online conducted in Asia. In the Malaysian, Korean, and Taiwanese studies, the predicted and observed 10-year overall survival differed by 6.7 %, 11.1 %, and 3.9 % correspondingly [1618]. The AUC of Adjuvant! Online was 0.73 (95 % CI, 0.69–0.77) in the Malaysian study and hence very close to the AUC of CancerMath reported in the present study [16]. Furthermore the prediction was too optimistic for young patients in almost all validation studies of Adjuvant! Online [12, 1517]. Although adjustment of 1.5-fold increase in risk was added to Adjuvant! Online version 7.0 for patients younger than 36 years and with ER positive breast cancer, overprediction was still found in recent validation studies [12, 16, 17]. Our findings from current validation of CancerMath also suggested that correction for young age at diagnosis is needed.

The selection of patients for validation can partially explain the discrepancy in observed and predicted survival. CancerMath has only been validated among patients with tumor size no more than 50 mm and positive nodes no more than seven [29]. In our validation dataset, 10 % of patients had tumor size larger than 50 mm and 8 % had more than ten positive nodes. However even for patients with tumor size in between 20 mm and 50 mm and one to three positive nodes, the difference between the predicted and observed survival was more than 3 %. In general, Asian patients are more likely to present with unfavorable prognostic features such as young age, negative hormone receptor status, HER2 overexpression, and more advanced stage compared to their western counterparts [3032]. In our current analysis, reduced agreement was observed for patients with poorer predicted outcome, especially for Malaysian patients, as illustrated by the calibration plot. In addition, the slope of the calibration plot for Malaysian patients were greater than 1 for the first three deciles which suggested that the spread of the predicted survival was less than observed survival. CancerMath’s poorer performance in Malaysia might be explained by higher proportion of patients in advanced stages and more heterogeneous prognosis in Malaysia. Such limitation of CancerMath may restrict its use to patients with better prognostic profile only. Furthermore CancerMath therapy calculator applies the same amount of risk reduction from adjuvant therapy as Adjuvant! Online, which was estimated from meta-analysis of clinical trials mainly conducted in western population [9, 19]. However non-adherence to treatment is more common among Asian women [3335]. Studies also reported different drug metabolism and toxicity induced by chemotherapy between Asian and Caucasian patients [36]. These evidences may imply CancerMath overestimate the effect of treatment in Asian patients.

Another possible explanation of suboptimal performance of CancerMath and also the limitation of our study is missing data on ER (6 %), PR (15 %), HER2 status (47 %), and tumor grade (11 %). For patients with complete information on required predictors (N = 1872), the predicted and observed 5-year survival was 86.0 % and 82.5 %. The difference were similar to what we observed in the entire dataset. Therefore the impact of missing data is relatively small on performance of CancerMath.

Several gene expression profiling assays, such as MammaPrint [37] and Oncotype Dx [38] are currently available for breast cancer prognostication and treatment decision. However these tools do not incorporate clinicopathologic factors which are readily available or relatively cheap to obtain. Due to the high cost of these tests and larger proportion of patients with high predicted risk in Asia [39, 40], the clinical utility is uncertain in this region. Therefore traditional prognostic model using clinicopathologic factors seems more reasonable in our local setting.

Conclusions

In conclusion, CancerMath demonstrated modest discrimination and calibration among Southeast Asian patients. Our results suggest that CancerMath is more suitable for patients diagnosed with favorable disease.

Abbreviations

AC: 

Doxorubicin and cyclophosphamide

AI: 

Aromatase inhibitors

AUC: 

Area under the curve

CI: 

Confidence interval

C-index: 

Concordance index

CMF: 

Cyclophosphamide, methotrexate and fluorouracil

ER: 

Estrogen receptor

FEC: 

Fluorouracil, epirubicin and cyclophosphamide

FISH: 

Fluorescence in situ hybridization

HER2: 

Human epidermal growth factor receptor 2

IHC: 

Immunohistochemistry

MR: 

Mortality ratio

NUH: 

National University Hospital

PR: 

Progesterone receptor

SEER: 

Surveillance, epidemiology and end-result

TAC: 

Docetaxel, doxorubicin and cyclophosphamide

TTSH: 

Tan Tock Seng Hospital

UMMC: 

University Malaya Medical Centre.

Declarations

Acknowledgement

Not applicable.

Funding

This study was supported by Clinician Scientist Award (NMRC/CSA/0048/2013) from National Medical Research Council, Singapore and a High Impact Research Grant (UM.C/HIR/MOHE/06) from the Ministry of Higher Education, Malaysia.

Availability of data and material

The datasets generated during and/or analysed during the current study are not publicly available due to potential breach of confidentiality but are available from the corresponding author on reasonable request.

Author’s contributions

HM, MH, HMV and NB were involved in study design, analysis and interpretation of data, and manuscript writing. NAT, HW, SS, CY, ET, PC and SL collected and interpreted data, critically reviewed the manuscript and provided suggestion for revision. All authors read and approved the final report before submission.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethics approval was obtained from Domain Specific Review Board under National Healthcare Group in Singapore and Medical Ethics Committee under University Malaya Medical Centre.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System
(2)
Department of Surgery, National University Hospital
(3)
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
(4)
Imaging Division, University Medical Center Utrecht
(5)
Department of Surgery, Faculty of Medicine, University of Malaya
(6)
Clinical Epidemiology Unit, National Clinical Research Centre
(7)
Department of Surgery, Tan Tock Seng Hospital
(8)
Department of Hematology Oncology, National University Cancer Institute, National University Health System
(9)
Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya
(10)
Julius Center for Health Sciences and Primary Care, University Medical Center

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