BACKGROUND: The objective of the current study was to formulate a scoring system to enable decision making for prophylactic stabilization of the femur after surgical resection of a soft tissue sarcoma (STS) of the thigh. METHODS: A logistic regression model was developed using patient variables collected from a prospectively collected database. The study group included 22 patients who developed a radiation-related pathological fracture of the femur after surgery and radiotherapy for an STS of the thigh. The control group of 79 patients received similar treatment but did not sustain a fracture. No patients received chemotherapy. The mean follow-up was 8.6 years. The variables examined were age, gender, tumor size, radiation dose (low [50 grays (Gy)] vs high [> or = 60 Gy]), extent of periosteal stripping (<10 cm, 10-20 cm, and >20 cm), and thigh compartment involvement (posterior, adductor, anterior or other [ie, abductors and groin]). RESULTS: On the basis of an optimal regression model, the ability to predict radiation-associated fracture risk was 91% sensitive and 81% specific. The area under the receiver operating characteristic curve was 0.9, which supports this model as a very accurate predictor of fracture risk. CONCLUSIONS: Radiation-related fractures of the femur after combined surgery and radiotherapy for STS are uncommon, but are difficult to manage and their nonunion rate is extremely high. The results of the current study suggest that it is possible to predict radiation-associated pathological fracture risk using patient and treatment variables with high sensitivity and specificity. This would allow for the identification of high-risk patients and treatment with either close follow-up or prophylactic intramedullary nail stabilization. The presentation of this model as a nomogram will facilitate its clinical use.
BACKGROUND: The objective of the current study was to formulate a scoring system to enable decision making for prophylactic stabilization of the femur after surgical resection of a soft tissue sarcoma (STS) of the thigh. METHODS: A logistic regression model was developed using patient variables collected from a prospectively collected database. The study group included 22 patients who developed a radiation-related pathological fracture of the femur after surgery and radiotherapy for an STS of the thigh. The control group of 79 patients received similar treatment but did not sustain a fracture. No patients received chemotherapy. The mean follow-up was 8.6 years. The variables examined were age, gender, tumor size, radiation dose (low [50 grays (Gy)] vs high [> or = 60 Gy]), extent of periosteal stripping (<10 cm, 10-20 cm, and >20 cm), and thigh compartment involvement (posterior, adductor, anterior or other [ie, abductors and groin]). RESULTS: On the basis of an optimal regression model, the ability to predict radiation-associated fracture risk was 91% sensitive and 81% specific. The area under the receiver operating characteristic curve was 0.9, which supports this model as a very accurate predictor of fracture risk. CONCLUSIONS: Radiation-related fractures of the femur after combined surgery and radiotherapy for STS are uncommon, but are difficult to manage and their nonunion rate is extremely high. The results of the current study suggest that it is possible to predict radiation-associated pathological fracture risk using patient and treatment variables with high sensitivity and specificity. This would allow for the identification of high-risk patients and treatment with either close follow-up or prophylactic intramedullary nail stabilization. The presentation of this model as a nomogram will facilitate its clinical use.
Authors: Joal D Beane; James C Yang; Donald White; Seth M Steinberg; Steven A Rosenberg; Udo Rudloff Journal: Ann Surg Oncol Date: 2014-04-23 Impact factor: 5.344
Authors: Claudia Andrä; Alexander Klein; Hans Roland Dürr; Josefine Rauch; Lars Hartwin Lindner; Thomas Knoesel; Martin Angele; Andrea Baur-Melnyk; Claus Belka; Falk Roeder Journal: Strahlenther Onkol Date: 2017-02-22 Impact factor: 3.621
Authors: Anne H Blaes; Bruce Lindgren; Daniel A Mulrooney; Lauren Willson; L Chinsoo Cho Journal: J Cancer Surviv Date: 2010-09-09 Impact factor: 4.442
Authors: Michael R Folkert; Dana L Casey; Sean L Berry; Aimee Crago; Nicola Fabbri; Samuel Singer; Kaled M Alektiar Journal: Ann Surg Oncol Date: 2019-01-31 Impact factor: 5.344