OBJECTIVE: To develop a multivariable model and online decision-support calculator to aid in preoperative discrimination of benign from malignant splenic masses in dogs. ANIMALS: 522 dogs that underwent splenectomy because of splenic masses. PROCEDURES: A multivariable model was developed with preoperative clinical data obtained retrospectively from the records of 422 dogs that underwent splenectomy. Inclusion criteria were the availability of complete abdominal ultrasonographic examination images and splenic histologic slides or histology reports for review. Variables considered potentially predictive of splenic malignancy were analyzed. A receiver operating characteristic curve was created for the final multivariable model, and area under the curve was calculated. The model was externally validated with data from 100 dogs that underwent splenectomy subsequent to model development and was used to create an online calculator to estimate probability of splenic malignancy in individual dogs. RESULTS: The final multivariable model contained 8 clinical variables used to estimate splenic malignancy probability: serum total protein concentration, presence (vs absence) of ≥ 2 nRBCs/100 WBCs, ultrasonographically assessed splenic mass diameter, number of liver nodules (0, 1, or ≥ 2), presence (vs absence) of multiple splenic masses or nodules, moderate to marked splenic mass inhomogeneity, moderate to marked abdominal effusion, and mesenteric, omental, or peritoneal nodules. Areas under the receiver operating characteristic curves for the development and validation populations were 0.80 and 0.78, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: The online calculator (T-STAT.net or T-STAT.org) developed in this study can be used as an aid to estimate the probability of malignancy in dogs with splenic masses and has potential to facilitate owners' decisions regarding splenectomy.
OBJECTIVE: To develop a multivariable model and online decision-support calculator to aid in preoperative discrimination of benign from malignant splenic masses in dogs. ANIMALS: 522 dogs that underwent splenectomy because of splenic masses. PROCEDURES: A multivariable model was developed with preoperative clinical data obtained retrospectively from the records of 422 dogs that underwent splenectomy. Inclusion criteria were the availability of complete abdominal ultrasonographic examination images and splenic histologic slides or histology reports for review. Variables considered potentially predictive of splenic malignancy were analyzed. A receiver operating characteristic curve was created for the final multivariable model, and area under the curve was calculated. The model was externally validated with data from 100 dogs that underwent splenectomy subsequent to model development and was used to create an online calculator to estimate probability of splenic malignancy in individual dogs. RESULTS: The final multivariable model contained 8 clinical variables used to estimate splenic malignancy probability: serum total protein concentration, presence (vs absence) of ≥ 2 nRBCs/100 WBCs, ultrasonographically assessed splenic mass diameter, number of liver nodules (0, 1, or ≥ 2), presence (vs absence) of multiple splenic masses or nodules, moderate to marked splenic mass inhomogeneity, moderate to marked abdominal effusion, and mesenteric, omental, or peritoneal nodules. Areas under the receiver operating characteristic curves for the development and validation populations were 0.80 and 0.78, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: The online calculator (T-STAT.net or T-STAT.org) developed in this study can be used as an aid to estimate the probability of malignancy in dogs with splenic masses and has potential to facilitate owners' decisions regarding splenectomy.
Authors: Katherine A Skorupski; Craig A Clifford; Melissa C Paoloni; Ana Lara-Garcia; Lisa Barber; Michael S Kent; Amy K LeBlanc; Aarti Sabhlok; Elizabeth A Mauldin; Frances S Shofer; C Guillermo Couto; Karin U Sørenmo Journal: J Vet Intern Med Date: 2007 Jan-Feb Impact factor: 3.333
Authors: Karin U Sorenmo; Jennifer L Baez; Craig A Clifford; Elizabeth Mauldin; Beth Overley; Katherine Skorupski; Roxanne Bachman; Marissa Samluk; Frances Shofer Journal: J Vet Intern Med Date: 2004 Mar-Apr Impact factor: 3.333
Authors: Stanley E Kim; Julius M Liptak; Trent T Gall; Gabrielle J Monteith; J Paul Woods Journal: J Am Vet Med Assoc Date: 2007-11-15 Impact factor: 1.936
Authors: Abhijit V Kshirsagar; Ya-Lin Chiu; Andrew S Bomback; Phyllis A August; Anthony J Viera; Romulo E Colindres; Heejung Bang Journal: J Clin Hypertens (Greenwich) Date: 2010-10 Impact factor: 3.738