Breast Cancer Treatment Evaluation based on Mammographic and Echographic Distance Computing
Accurate assessment of the primary tumor response to
treatment is important in the management of breast cancer. This
paper introduces a new set of treatment evaluation indicators for
breast cancer cases based on the computational process of three
known metrics, the Euclidian, Hamming and Levenshtein distances.
The distance principals are applied to pairs of mammograms and/or
echograms, recorded before and after treatment, determining a
reference point in judging the evolution amount of the studied
carcinoma. The obtained numerical results are indeed very
transparent and indicate not only the evolution or the involution of
the tumor under treatment, but also a quantitative measurement of the
benefit in using the selected method of treatment.
[1] B.K. Verma, Heredity & Cancer: Breast cancer as a model, IASTED
RM-96, Hawaii, USA, 1999, pp. 84-88.
[2] Carlos Andres Pena et all, Proceedings Information Processing in
Medical Imaging, IPMI"03, 2003.
[3] K. S. Woods, C. C. Doss, Comparative Evaluation of Pattern
Recognition Techniques M 16L-171PI for Detection of
Microcalcifications in Mammography, International Journal of Pattern
Recognition and Artificial Intelligence, 1993.
[4] K.S. Woods, C.C Doss, Comparative Evaluation of Pattern Recognition
Techniques for Detection of Microcalcifications in Mammography,
International Journal of Pattern Recognition and Artificial Intelligence,
2003, pp. 80-85.
[5] Fitzgibbons PL, Page DL, Weaver D, et all, Prognostic factors in breast
cancer, pp. 124:966-978, College of American Pathologists Consensus
Statement. Arch Pathol Lab Med, 1999, 2000.
[6] Mirza AN, Mirza NQ, Vlastos G, et all, Prognostic factors in nodenegative
breast cancer: a review of studies with sample size more than
200 and follow-up more than 5 years, Ann Surg, 2002, 235:10-26.
[7] Greene FL, Page DL, Fleming ID, et all, AJCC Cancer Staging
Handbook. TNM Classification of Malignant Tumors, 6th ed. New York:
Springer Verlag, 2002.
[8] Sobin LH, Wittekind CH, eds. TNM, Classification of Malignant
Tumours, 6th ed. New York: John Wiley & Sons, 2002.
[9] Adair F, Berg J, Joubert L, et all, Long-term follow-up of breast cancer
patients: the 30-year report, Cancer, 1974, pp. 33:1145-1150.
[10] Abraham. Kandel, Fuzzy Expert Systems, CRC Press, 2002.
[11] Lance Chambers, Practical Handbook of Genetic Algorithms, CRC
Press, 2004.
[12] Stephen I. Gallant, Neural Network Learning and Expert Systems, MIT
Press, 2006.
[13] *** Case studies and clinical documentation, Institute of Oncology
Bucharest.
[1] B.K. Verma, Heredity & Cancer: Breast cancer as a model, IASTED
RM-96, Hawaii, USA, 1999, pp. 84-88.
[2] Carlos Andres Pena et all, Proceedings Information Processing in
Medical Imaging, IPMI"03, 2003.
[3] K. S. Woods, C. C. Doss, Comparative Evaluation of Pattern
Recognition Techniques M 16L-171PI for Detection of
Microcalcifications in Mammography, International Journal of Pattern
Recognition and Artificial Intelligence, 1993.
[4] K.S. Woods, C.C Doss, Comparative Evaluation of Pattern Recognition
Techniques for Detection of Microcalcifications in Mammography,
International Journal of Pattern Recognition and Artificial Intelligence,
2003, pp. 80-85.
[5] Fitzgibbons PL, Page DL, Weaver D, et all, Prognostic factors in breast
cancer, pp. 124:966-978, College of American Pathologists Consensus
Statement. Arch Pathol Lab Med, 1999, 2000.
[6] Mirza AN, Mirza NQ, Vlastos G, et all, Prognostic factors in nodenegative
breast cancer: a review of studies with sample size more than
200 and follow-up more than 5 years, Ann Surg, 2002, 235:10-26.
[7] Greene FL, Page DL, Fleming ID, et all, AJCC Cancer Staging
Handbook. TNM Classification of Malignant Tumors, 6th ed. New York:
Springer Verlag, 2002.
[8] Sobin LH, Wittekind CH, eds. TNM, Classification of Malignant
Tumours, 6th ed. New York: John Wiley & Sons, 2002.
[9] Adair F, Berg J, Joubert L, et all, Long-term follow-up of breast cancer
patients: the 30-year report, Cancer, 1974, pp. 33:1145-1150.
[10] Abraham. Kandel, Fuzzy Expert Systems, CRC Press, 2002.
[11] Lance Chambers, Practical Handbook of Genetic Algorithms, CRC
Press, 2004.
[12] Stephen I. Gallant, Neural Network Learning and Expert Systems, MIT
Press, 2006.
[13] *** Case studies and clinical documentation, Institute of Oncology
Bucharest.
@article{"International Journal of Medical, Medicine and Health Sciences:51309", author = "M. Caramihai and Irina Severin and H. Balan and A. Blidaru and V. Balanica", title = "Breast Cancer Treatment Evaluation based on Mammographic and Echographic Distance Computing", abstract = "Accurate assessment of the primary tumor response to
treatment is important in the management of breast cancer. This
paper introduces a new set of treatment evaluation indicators for
breast cancer cases based on the computational process of three
known metrics, the Euclidian, Hamming and Levenshtein distances.
The distance principals are applied to pairs of mammograms and/or
echograms, recorded before and after treatment, determining a
reference point in judging the evolution amount of the studied
carcinoma. The obtained numerical results are indeed very
transparent and indicate not only the evolution or the involution of
the tumor under treatment, but also a quantitative measurement of the
benefit in using the selected method of treatment.", keywords = "Breast cancer, Distance metrics, Cancer treatment
evaluation.", volume = "3", number = "8", pages = "180-5", }