INTRODUCTION: The use of neoadjuvant chemotherapy in cases of locally advanced breast cancer has been steadily increasing, and is also in wider use for other cancers. As a consequence, a growing number of studies have focused on the question of how best to assess the therapeutic response to various chemotherapy or systemic therapy regimens. Prognostic imaging of response to therapy early in the course of a planned chemotherapy regimen could be of considerable value, particularly if shifting to another therapy regimen would be more effective. METHODS: A cost effectiveness analysis was completed, specific to imaging of neoadjuvant chemotherapy response in breast cancer, to determine the dominant parameters that would make imaging systems cost effective. The cost analysis was completed with respect to a system for near infrared spectral imaging, but the costs are not dramatically different for other systems such as PET or MRI. Using a standard metric of $25,000 per discounted life year gained as a measure of a successful system. RESULTS: It is shown that system specificity and patient average life expectancy are not dominant factors. Increases in cure rate and the efficacy of the initial chemotherapy are dominant factors. As long as the initial chemotherapy was less than 90% effective, most imaging systems would be cost effective, and if the cure rate of the disease could be increased as little as 1% through a change to alternate therapy, then the cost effectiveness of the system would be acceptable. CONCLUSIONS: Based upon this simple economic analysis, diagnostic imaging of neoadjuvant chemotherapy appears warranted, assuming that it can be shown that the early shift from ineffective neoadjuvant chemotherapy to a more effective one has a measurable benefit in cure rate. This study indicates that the most important issue is to assess the added benefit of individualized chemotherapy in patient management, and clinical trials in this area would then provide the data required to justify analysis of prognostic imaging procedures.
INTRODUCTION: The use of neoadjuvant chemotherapy in cases of locally advanced breast cancer has been steadily increasing, and is also in wider use for other cancers. As a consequence, a growing number of studies have focused on the question of how best to assess the therapeutic response to various chemotherapy or systemic therapy regimens. Prognostic imaging of response to therapy early in the course of a planned chemotherapy regimen could be of considerable value, particularly if shifting to another therapy regimen would be more effective. METHODS: A cost effectiveness analysis was completed, specific to imaging of neoadjuvant chemotherapy response in breast cancer, to determine the dominant parameters that would make imaging systems cost effective. The cost analysis was completed with respect to a system for near infrared spectral imaging, but the costs are not dramatically different for other systems such as PET or MRI. Using a standard metric of $25,000 per discounted life year gained as a measure of a successful system. RESULTS: It is shown that system specificity and patient average life expectancy are not dominant factors. Increases in cure rate and the efficacy of the initial chemotherapy are dominant factors. As long as the initial chemotherapy was less than 90% effective, most imaging systems would be cost effective, and if the cure rate of the disease could be increased as little as 1% through a change to alternate therapy, then the cost effectiveness of the system would be acceptable. CONCLUSIONS: Based upon this simple economic analysis, diagnostic imaging of neoadjuvant chemotherapy appears warranted, assuming that it can be shown that the early shift from ineffective neoadjuvant chemotherapy to a more effective one has a measurable benefit in cure rate. This study indicates that the most important issue is to assess the added benefit of individualized chemotherapy in patient management, and clinical trials in this area would then provide the data required to justify analysis of prognostic imaging procedures.
Authors: Eric L Rosen; Kimberly L Blackwell; Jay A Baker; Mary Scott Soo; Rex C Bentley; Daohai Yu; Thaddeus V Samulski; Mark W Dewhirst Journal: AJR Am J Roentgenol Date: 2003-11 Impact factor: 3.959
Authors: Albert Cerussi; David Hsiang; Natasha Shah; Rita Mehta; Amanda Durkin; John Butler; Bruce J Tromberg Journal: Proc Natl Acad Sci U S A Date: 2007-02-28 Impact factor: 11.205
Authors: R M L Warren; L G Bobrow; H M Earl; P D Britton; D Gopalan; A D Purushotham; G C Wishart; J R Benson; W Hollingworth Journal: Br J Cancer Date: 2004-04-05 Impact factor: 7.640
Authors: H Y Ban; M Schweiger; V C Kavuri; J M Cochran; L Xie; D R Busch; J Katrašnik; S Pathak; S H Chung; K Lee; R Choe; B J Czerniecki; S R Arridge; A G Yodh Journal: Med Phys Date: 2016-07 Impact factor: 4.071
Authors: David Mitchell; Carrie B Hruska; Judy C Boughey; Dietlind L Wahner-Roedler; Katie N Jones; Cindy Tortorelli; Amy Lynn Conners; Michael K O'Connor Journal: Clin Nucl Med Date: 2013-12 Impact factor: 7.794