Brieman, L., Friedman, J., Olshen, R. and Stone, C., (1984), Classification and Regression Trees, Wadworth, Belmont, CA.
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1 Bibliography: Brieman, L., Friedman, J., Olshen, R. and Stone, C., (1984), Classification and Regression Trees, Wadworth, Belmont, CA. Breiman, L, (1999, )Random Forests Statistics Department, University California Berkley CA Brieman, L.,(2001), Statistical modeling: The two cultures, Statist. Sci. 16, No. 3, Brieman, L.,(2005) Correspondence. Gamel, J., McLean, I. and Greenberg, R., (1988), Interval-by-interval Cox model analysis of 3680 cases of intraocular melanomas Shows a decline in the prognostic value of size and cell type over time and tumor excision, Cancer 61: Gamel, J., Greenberg, R. and McLean I., (1998), A stable linear algorithm for fitting the lognormal model to survival data, Computers and Biomedical Research No31:38-47 Gamel, J., George, S., Edwards, M. and Seigler, H., (2002),The long-term clinical course of patients with cutaneous melanoma, Cancer, 95, No. 6: Hofstadter, D., (1979), Godel, Escher, Bach: an Eternal Golden Braid, Basic Books Inc, New York Seigler (2005) Personal Communication Slingluff, C., Vollmer, R., Reintgen, D. and Seigler, H. Lethal Thin malignant melanoma: Identifying patients at risk, Ann. Surg. 1988;208: No2, Stadelmann, W., Rapaport, D., Soong, S. et al., (1998), Prognostic factors that influence melanoma outcome. In; Balch, C.,Houghton, A., Sober,A., eds. Cutaneous Melanoma, 3 rd ed.,st Louis, MO: Quality Medical Publishing; Venables, W. and Ripley, B. Modern Applied Statistics with S-PLUS, Springer-Verlag, New York, Inc. Vollmer, R. and Seigler, H., (2001A), A model for pretest probability of lymph node metastasis from cutaneous melanoma, Am. J. Clin. Pathol. 114: Vollmer, R. and Seigler, H., (2001B), Using a continuous transformation of the Breslow thickness for prognosis in cutaneous melanoma, Am. J. Clin. Pathol. 115:
2 R 2.01 (2005), The R Foundation for Statistical Computing, cran@r-project.org Packages Sarkar, D. (2004) Lattice Graphics, Implementation of Trellis Graphics Breiman, L., Cutler, A., Liaw, A. and Wiener, M. randomforest:,(2005) Breiman and Cutler s random forests for classification and regres Ripley, B. (2005) tree: Classification and regression trees 61
3 APPENDIX 62
4 deviance Fig.1 Cross Validation Deviance Plot size Inf misclass Fig.2 Cross Validation Misclassification Plot size 63
5 Fig.3. Plot of Full Tree without Text, All Patients, All Variables and Any Recurrence 64
6 dextent:abc stggrp:ab typcas:a Fig.4. Plot of Tree All Patients, All Variables and Any Recurrence. Pruned Tree k=30 dextent:abc stggrp:ab stggrp:bc typcas:a prisite:bcde clark:f anyimm:a side:b prisite:abcfghi hist:abcegh clark:ac AGE < 37.2 prisite:bcg anyimm:a clark:bc clark:bce AGE < Fig.5. Plot of Tree All Patients All Variables and Any Recurrence Pruned Tree k=8 65
7 MeanDecreaseAccurac MeanDecreaseAccurac dextent stggrp hist histgrp clark satel race prisite ulcer AGE THICK side typcas sex anyimm dextent stggrp histgrp hist race satel prisite clark AGE ulcer side typcas sex anyimm THICK Importance Importance Fig.6. The Variable Importance Plot of All Patients, All Variables and Any Recurrence (L). The Variable Importance Plot of All Patients, All Variables and Recurrence More Than Local (R). 66
8 MeanDecreaseAccurac MeanDecreaseAccurac prisite hist histgrp clark race side ulcer AGE sex anyimm THICK satel prisite AGE hist race side histgrp ulcer sex anyimm satel clark THICK Importance Importance Fig.7. The Variable Importance Plot of All Patients, Leave Out STGGRP, DEXTEXT and TYPCAS. Any Recurrence (L). The Variable Importance Plot of All Patients, Leave Out STGGRP, DEXTEXT and TYPCAS, Recurrence More Than Local(R) 67
9 MeanDecreaseAccuracy MeanDecreaseAccurac dextent hist histgrp clark AGE race prisite ulcer satel side THICK typcas sex anyimm stggrp dextent hist histgrp ulcer race side prisite satel sex stggrp anyimm AGE THICK clark Importance Importance Fig.8. The Variable Importance Plot of Limited Patients, All Variables and Any Recurrence (L). The Variable Importance Plot of Limited Patients, All Variables and Recurrence More Than Local (R) 68
10 TEXT FULL TREE ALL PATIENTS ALL VARIABLES ANY RECURRENCE 1) root ( ) 2) dextent: 0,1, ( ) 4) stggrp: 0, ( ) 8) typcas: ( ) 16) anyimm: ( ) 32) prisite: 1,2,3,6,7,12, ( ) 64) AGE < ( ) 128) satel: ( ) * 129) satel: ( ) 258) AGE < ( ) 516) AGE < ( ) * 517) AGE > ( ) * 259) AGE > ( ) * 65) AGE > ( ) 130) AGE < ( ) * 131) AGE > ( ) 262) histgrp: 1,2,3, ( ) 524) prisite: 1,2,3,7, ( ) 1048) AGE < ( ) 2096) AGE < ( ) * 2097) AGE > ( ) 4194) side: ( ) * 4195) side: ( ) * 1049) AGE > ( ) * 525) prisite: ( ) 1050) sex: ( ) * 1051) sex: ( ) 2102) AGE < ( ) * 2103) AGE > ( ) * 263) histgrp: 4, ( ) 526) sex: ( ) 1052) AGE < ( ) * 1053) AGE > ( ) * 527) sex: ( ) * 33) prisite: 4, ( ) 66) histgrp: 2,4, ( ) 132) AGE < ( ) * 133) AGE > ( ) * 67) histgrp: 1, ( ) 134) clark: ( ) * 135) clark: 1, ( ) * 17) anyimm: ( ) 34) hist: 1,2,3,6,12, ( ) 68) prisite: 2,3, ( ) 136) clark: 2,4,5, ( ) 272) side: ( ) Fig.9. Text Full Tree All Patients All Variables Any Recurrence 69
11 544) prisite: 2, ( ) * 545) prisite: ( ) 1090) THICK < ( ) * 1091) THICK > ( ) 2182) THICK < ( ) * 2183) THICK > ( ) 4366) AGE < ( ) * 4367) AGE > ( ) 8734) AGE < ( ) * 8735) AGE > ( ) * 273) side: ( ) * 137) clark: 1, ( ) 274) THICK < ( ) 548) sex: ( ) * 549) sex: ( ) 1098) AGE < ( ) 2196) AGE < ( ) * 2197) AGE > ( ) * 1099) AGE > ( ) * 275) THICK > ( ) 550) THICK < ( ) * 551) THICK > ( ) 1102) histgrp: 1,3, ( ) * 1103) histgrp: ( ) 2206) AGE < ( ) 4412) AGE < ( ) 8824) AGE < ( ) 17648) AGE < ( ) 35296) AGE < ( ) 70592) side: ( ) ) THICK < ( ) ) satel: 1, ( ) * ) satel: ( ) * ) THICK > ( ) ) ulcer: 1, ( ) ) THICK < ( ) * ) THICK > ( ) ) THICK < ( ) * ) THICK > ( ) * ) ulcer: ( ) ) THICK < ( ) * ) THICK > ( ) * 70593) side: ( ) ) THICK < ( ) ) THICK < ( ) ) THICK < ( ) * ) THICK > ( ) ) AGE < ( ) * ) AGE > ( ) * Fig.9. cont 70
12 282373) THICK > ( ) * ) THICK > ( ) ) THICK < ( ) ) AGE < ( ) * ) AGE > ( ) * ) THICK > ( ) ) ulcer: 2, ( ) ) sex: ( ) ) THICK < ( ) * ) THICK > ( ) ) THICK < ( ) ) THICK < ( ) ) AGE < ( ) * ) AGE > ( ) * ) THICK > ( ) * ) THICK > ( ) * ) sex: ( ) ) THICK < ( ) ) AGE < ( ) * ) AGE > ( ) ) AGE < ( ) ) AGE < ( ) ) AGE < ( ) ) AGE > ( ) * ) AGE > ( ) * ) AGE > ( ) ) AGE < ( ) * ) AGE > ( ) * ) THICK > ( ) * ) ulcer: ( ) * 35297) AGE > ( ) * 17649) AGE > ( ) * 8825) AGE > ( ) * 4413) AGE > ( ) * 2207) AGE > ( ) 4414) AGE < ( ) * 4415) AGE > ( ) * 69) prisite: 1,6,12,13, ( ) 138) clark: 2,3, ( ) 276) prisite: 1,13, ( ) 552) AGE < ( ) * 553) AGE > ( ) 1106) AGE < ( ) 2212) prisite: ( ) 4424) histgrp: ( ) 8848) clark: ( ) * 8849) clark: ( ) * 4425) histgrp: ( ) * 2213) prisite: ( ) * Fig.9. cont 71
13 1107) AGE > ( ) 2214) side: 0, ( ) 4428) THICK < ( ) 8856) AGE < ( ) * 8857) AGE > ( ) 17714) AGE < ( ) 35428) THICK < ( ) * 35429) THICK > ( ) 70858) AGE < ( ) ) AGE < ( ) ) sex: ( ) * ) sex: ( ) * ) AGE > ( ) * 70859) AGE > ( ) * 17715) AGE > ( ) * 4429) THICK > ( ) 8858) histgrp: 1,2,3, ( ) 17716) AGE < ( ) 35432) AGE < ( ) 70864) sex: ( ) * 70865) sex: ( ) ) AGE < ( ) * ) AGE > ( ) ) histgrp: ( ) * ) histgrp: ( ) ) AGE < ( ) * ) AGE > ( ) * 35433) AGE > ( ) 70866) AGE < ( ) * 70867) AGE > ( ) ) AGE < ( ) * ) AGE > ( ) * 17717) AGE > ( ) 35434) AGE < ( ) * 35435) AGE > ( ) 70870) AGE < ( ) * 70871) AGE > ( ) * 8859) histgrp: ( ) * 2215) side: ( ) 4430) AGE < ( ) 8860) AGE < ( ) 17720) THICK < ( ) 35440) sex: ( ) 70880) AGE < ( ) * 70881) AGE > ( ) ) THICK < ( ) * ) THICK > ( ) * 35441) sex: ( ) 70882) AGE < ( ) * Fig.9. cont 72
14 70883) AGE > ( ) ) AGE < ( ) ) ulcer: ( ) * ) ulcer: 1, ( ) * ) AGE > ( ) * 17721) THICK > ( ) * 8861) AGE > ( ) 17722) prisite: ( ) 35444) satel: ( ) * 35445) satel: ( ) 70890) AGE < ( ) * 70891) AGE > ( ) * 17723) prisite: ( ) * 4431) AGE > ( ) 8862) sex: ( ) 17724) AGE < ( ) 35448) AGE < ( ) 70896) AGE < ( ) ) ulcer: 2, ( ) ) prisite: 1, ( ) * ) prisite: ( ) ) AGE < ( ) * ) AGE > ( ) * ) ulcer: ( ) * 70897) AGE > ( ) * 35449) AGE > ( ) * 17725) AGE > ( ) 35450) AGE < ( ) 70900) AGE < ( ) * 70901) AGE > ( ) ) AGE < ( ) * ) AGE > ( ) * 70901) AGE > ( ) ) AGE < ( ) * ) AGE > ( ) * 35451) AGE > ( ) * 8863) sex: ( ) * 277) prisite: 6, ( ) 554) AGE < ( ) * 555) AGE > ( ) 1110) THICK < ( ) * 1111) THICK > ( ) * 139) clark: 1, ( ) 278) AGE < ( ) * 279) AGE > ( ) 558) clark: ( ) 1116) ulcer: 1, ( ) * 1117) ulcer: ( ) * 559) clark: ( ) Fig.9. cont 73
15 1118) ulcer: ( ) 2236) THICK < ( ) 4472) histgrp: 1,2, ( ) 8944) prisite: 1,12, ( ) 17888) AGE < ( ) 35776) sex: ( ) 71552) THICK < ( ) ) THICK < ( ) * ) THICK > ( ) ) AGE < ( ) * ) AGE > ( ) * 71553) THICK > ( ) * 35777) sex: ( ) * 17889) AGE > ( ) * 8945) prisite: ( ) * 4473) histgrp: ( ) * 2237) THICK > ( ) 4474) AGE < ( ) * 4475) AGE > ( ) 8950) prisite: 1, ( ) * 8951) prisite: ( ) * 1119) ulcer: 1, ( ) 2238) sex: ( ) * 2239) sex: ( ) * 35) hist: 4,10, ( ) 70) clark: 2,4, ( ) 140) THICK < ( ) * 141) THICK > ( ) * 9) typcas: ( ) 18) side: ( ) 36) clark: 1, ( ) 72) AGE < ( ) * 73) AGE > ( ) 146) hist: 2, ( ) 292) THICK < ( ) 584) AGE < ( ) * 585) AGE > ( ) * 293) THICK > ( ) 586) AGE < ( ) 1172) sex: ( ) * 1173) sex: ( ) * 587) AGE > ( ) * 147) hist: 3, ( ) * 37) clark: 2, ( ) 74) anyimm: ( ) * 75) anyimm: ( ) * 19) side: 0, ( ) 38) AGE < ( ) 76) clark: 2, ( ) Fig.9. cont 74
16 152) AGE < ( ) * 153) AGE > ( ) * 77) clark: 1, ( ) * 39) AGE > ( ) 78) AGE < ( ) 156) AGE < ( ) * 157) AGE > ( ) 314) prisite: 3, ( ) * 315) prisite: 1, ( ) 630) AGE < ( ) * 631) AGE > ( ) * 79) AGE > ( ) * 5) stggrp: 4, ( ) 10) prisite: 2,3,4, ( ) 20) THICK < ( ) 40) sex: ( ) 80) clark: 2,3, ( ) * 81) clark: ( ) * 41) sex: ( ) * 21) THICK > ( ) * 11) prisite: 1,12,13, ( ) 22) satel: ( ) 44) prisite: 1, ( ) 88) AGE < ( ) 176) AGE < ( ) 352) AGE < ( ) 704) THICK < ( ) * 705) THICK > ( ) 1410) AGE < ( ) * 1411) AGE > ( ) 2822) THICK < ( ) 5644) anyimm: ( ) * 5645) anyimm: ( ) 11290) AGE < ( ) 22580) AGE < ( ) * 22581) AGE > ( ) * 11291) AGE > ( ) * 2823) THICK > ( ) * 353) AGE > ( ) * 177) AGE > ( ) * 89) AGE > ( ) 178) AGE < ( ) * 179) AGE > ( ) 358) AGE < ( ) * 359) AGE > ( ) 718) AGE < ( ) * 719) AGE > ( ) * 45) prisite: 12, ( ) * 23) satel: 1, ( ) * Fig.9. cont 75
17 3) dextent: 3,4,5,6, ( ) 6) stggrp: 1, ( ) 12) prisite: ( ) 24) AGE < ( ) * 25) AGE > ( ) * 13) prisite: 2,3,13, ( ) * 7) stggrp: ( ) 14) clark: ( ) 28) prisite: ( ) * 29) prisite: 2, ( ) * 15) clark: 1,2,3,4, ( ) * Fig.9. cont 76
18 Table Consecutive Random Forests 10 Trees SAMPLE # MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR Table Consecutive Random Forests 25 Trees SAMPLE # MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR 77
19 Table Consecutive Random Forests 50 Trees SAMPLE # MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR Table Consecutive Random Forests 100 Trees SAMPLE # MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR 78
20 Table Consecutive Random Forests 200 Trees SAMPLE # MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR Table Consecutive Random Forests 300 Trees SAMPLE # MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR 79
21 Table Consecutive Random Forests 500 Trees SAMPLE # MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR Table Consecutive Random Forests 1000 Trees MTRY 4 SAMPLE # VOTES VOTES TOTAL PERCENT N Y N Y ERROR
22 Table 9. Consecutive Larger Trees NUMBER TREES 5 Consecutive runs 2000 trees 3 Consecutive runs 3000 trees 2 Consecutive runs 5000 trees MTRY 4 VOTES VOTES N Y N Y TOTAL PERCENT ERROR 81
23 Table 10. Single Tree Results All Patients All Variables Any Recurrence SINGLE TREE RESULTS ALL PATIENTS ALL VARIABLES ANY RECURRENCE TOTAL SET TOTAL PERCENT NUMBER OF ACTUAL, AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.50 FULL TREE k=5 k=30 of of TOTAL THIS IS RESULTS WITH >=.15 FULL TREE k=5 k=30 of of TOTAL THIS IS RESULTS WITH >=.10 FULL TREE k=5 k=30 of of TOTAL THIS IS RESULTS WITH >=.05 FULL TREE k=5 k=30 of of TOTAL
24 Table 11. Single Tree Results All Patients All Variables Recurrence More Than Local SINGLE TREE RESULTS ALL PATIENTS ALL VARIABLES RECURRENCE MORE THAN LOCAL TOTAL SET TOTAL PERCENT NUMBER OF ACTUAL, AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.50 FULL TREE k=5 k=30 of of TOTAL THIS IS RESULTS WITH >.15 FULL TREE k=5 k=30 of of TOTAL THIS IS RESULTS WITH >.1 FULL TREE k=5 k=30 of of TOTAL THIS IS RESULTS WITH >.05 FULL TREE k=5 k=30 of of TOTAL
25 Table12. Single Tree Results All Patients All Variables Recurrence More Than Local 2 nd Run SINGLE TREE ALL PATIENTS ALL VARIABLES RECURRENCE MORE THAN LOCAL 2 ND RUN TOTAL SET TOTAL PERCENT NUMBER OF ACTUAL, AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.50 FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >.15 FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >.10 FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >.05 FULL TREE k=5 k=30 of of TOTAL M.S.E
26 Table 13. Single Tree Results All Patients Leave Out Variables Any Recurrence SINGLE TREE RESULTS All PATIENTS LEAVE OUT VARIABLES ANY RECURRENCE TOTAL SET PERCENT NUMBER OF ACTUAL AND AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.50 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.15 FULL TREE k=5 k=30 of of THIS IS RESULTS WITH >=.1 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.05 FULL TREE k=5 k=30 of of for Full
27 Table 14. Single Tree Results All Patients Leave Out Variables Any Recurrence 2 nd Run SINGLE TREE RESULTS All PATIENTS LEAVE OUT VARIABLES RECURRENCE MORE THAN LOCAL 2 nd RUN TOTAL SET PERCENT NUMBER OF ACTUAL AND AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.5 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.15 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.10 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.05 FULL TREE k=5 k=30 of of for Full
28 Table 15. Single Tree Results All Patients Leave Out Variables Recurrence More Than Local SINGLE TREE All PATIENTS LEAVE OUT VARIABLES ANY RECURRENCE TOTAL SET PERCENT NUMBER OF ACTUAL AND AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.5" FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.15" FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.1" FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.05" FULL TREE k=5 k=30 of of for Full
29 Table 16. Single Tree Results All Patients Leave Out Variables Recurrence More Than Local 2 nd Run SINGLE TREE All PATIENTS LEAVE OUT VARIABLES RECURRENCE MORE THAN LOCAL 2 nd RUN TOTAL SET PERCENT NUMBER OF ACTUAL AND AND PERCENT IN RANDOM SAMPLE PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.50 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.15 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.10 FULL TREE k=5 k=30 of of for Full THIS IS RESULTS WITH >=.05 FULL TREE k=5 k=30 of of for Full
30 Table 17. Single Tree Results Limited Patients All Variables Any Recurrence SINGLE TREE RESULTS LIMITED PATIENTS ANY RECURRENCE TOTAL SET TOTAL PERCENT NUMBER OF ACTUAL, AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.5" FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >=.15" FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >=.1" FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >=.05" FULL TREE k=5 k=30 of of TOTAL M.S.E
31 Table 18. Single Tree Results Limited Patients All Variables Recurrence More Than Local SINGLE TREE RESULTS LIMITED PATIENTS RECURRENCE MORE THAN LOCAL TOTAL SET TOTAL PERCENT NUMBER OF ACTUAL, AND PERCENT IN RANDOM SAMPLES PERCENT PERCENT PREDICTED WITH >=.50 IS >=.15 IS >=.10 IS >=.05 IS THIS IS RESULTS WITH >=.5" FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >=.15" FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >=.1" FULL TREE k=5 k=30 of of TOTAL M.S.E THIS IS RESULTS WITH >=.5" FULL TREE k=5 k=30 of of TOTAL M.S.E
32 Table 19. RF All Patients All Variables Any Recurrence TREES 300 MTRY = 15 T.N. CUTOFF N Y TOTAL ERROR
33 MTRY 13 T.N. CUTOFF N Y TOTAL ERROR Table 19 cont 92
34 MTRY 10 T.N. CUTOFF N Y TOTAL ERROR Table 19 cont 93
35 MTRY 7 T.N. CUTOFF N Y TOTAL ERROR Table 19 cont 94
36 MTRY 4 T.N. CUTOFF TOTAL N Y ERROR Table 19 cont 95
37 MTRY 2 T.N. CUTOFF TOTAL N Y ERROR Table 19 cont 96
38 MTRY 1 T.N. CUTOFF TOTAL N Y ERROR Table 19 cont 97
39 Table 20. RF All Patients All Variables Recurrence More Than Local TREES 300 MTRY = 15 T.N. CUTOFF N Y M.S.E TOTAL ERROR
40 MTRY 13 T.N. CUTOFF M.S.E TOTAL N Y ERROR Table 20 cont 99
41 MTRY 10 T.N. CUTOFF N Y M.S.E TOTAL ERROR Table 20 cont 100
42 MTRY 7 T.N. CUTOFF N Y M.S.E TOTAL ERROR Table 20 cont 101
43 MTRY 4 T.N. CUTOFF N Y M.S.E TOTAL ERROR Table 20 cont 102
44 MTRY 2 T.N. CUTOFF N Y M.S.E TOTAL ERROR Table 20 cont 103
45 MTRY 1 T.N. CUTOFF N Y M.S.E TOTAL ERROR Table 20 cont 104
46 Table 21. RF All Patients Leave Out STAGE GROUP, DETEXT AND TYPCAS Any Recurrence TREES 300 MTRY = 12 T.N. CUTOFF N Y TOTAL ERROR Table 21 cont 105
47 MTRY 10 T.N. CUTOFF TOTAL N Y ERROR Table 21 cont 106
48 MTRY 4 T.N. CUTOFF N Y TOTAL ERROR Table 21 cont 107
49 MTRY 2 T.N. CUTOFF N Y TOTAL ERROR Table 21 cont 108
50 Table 22. RF All Patients Leave Out STAGE GROUP, DETEXT And TYPCAS Recurrence More Than Local TREES 300 MTRY = 12 T.N. CUTOFF N Y TOTAL ERROR
51 MTRY 10 T.N. CUTOFF TOTAL N Y ERROR Table 22 cont 110
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