Biomedical subjects
Pankaj Kalra
Publications and source records attributed to Pankaj Kalra.
Outcomes of hand-assisted laparoscopic nephrectomy in technically challenging cases.
OBJECTIVES: To evaluate the outcomes of hand-assisted laparoscopic nephrectomy in patients with significant complicating clinical factors. METHODS: We performed a retrospective review of 322 hand-assisted laparoscopic nephrectomy cases that were completed at a single institution from 1998 to 2004. Patients with a history of extensive abdominal surgery or prior procedures on the affected kidney, evidence of perirenal inflammation, renal lesions 10 cm or more in diameter, or level I renal vein thrombus were included. RESULTS: A total of 42 patients were included in this series. Of these, 16 patients had a lesion 10 cm or larger, 10 had a renal vein thrombus, and 10 had undergone prior major abdominal surgery. Many patients had more than one complicating factor. Another 6 patients had a history of prior renal procedures or chronic inflammatory processes involving the affected kidney. One Stage T4 renal tumor with paraspinous muscle invasion was successfully managed without conversion. The overall mean operative time and estimated blood loss was 235 minutes and 439 mL, respectively, with a mean hospital stay of 4 days. Four patients (9.5%) required open conversion (one renal hilar injury, two failure to progress, and one persistent bleeding from the renal fossa). Postoperative complications included pulmonary embolism in 1, ileus in 1, and chronic obstructive pulmonary disease exacerbation in 1 patient. One patient developed an incarcerated port site hernia requiring reoperation. CONCLUSIONS: Hand-assisted laparoscopic nephrectomy is an attractive minimally invasive option in the setting of significant complicating factors. This technique may facilitate the successful laparoscopic completion of these challenging cases with reasonable operative times, blood loss, and complication rates.
A neurocomputational model for prostate carcinoma detection.
BACKGROUND: Current guidelines for prostate carcinoma screening rely primarily on the digital rectal examination (DRE) and prostate specific antigen (PSA). Well described patient risk factors for prostate carcinoma also include age, ethnicity, family history, and complexed PSA. However, due to the nonlinear relation of each of these variables with prostate carcinoma, it is difficult to predict reliably each patient's risk based on linear univariate analysis. The authors investigated a neural network to model the risk of prostate carcinoma by seven readily available clinical features. METHODS: The database for the current study comprised 3268 men recently evaluated for the early detection of prostate carcinoma. The seven clinical features evaluated included age, race, family history, International Prostate Symptom Score (IPSS), DRE, and total and complexed PSA. Three hundred forty-eight subjects in the dataset included men with determined prostate biopsy outcomes and for whom at least 6 of 7 features were available. The dataset was divided randomly into a training set (60%) and a test set (40%), with n1/n2 cross-validation used to evaluate model accuracy, and was modeled with linear and quadratic discriminant function analysis and a neural computational system. After a model with acceptable goodness of fit was achieved, reverse regression analysis using Wilks's generalized likelihood ratio test was performed to evaluate the statistical significance of each input variable. RESULTS: The receiving operating characteristic (ROC) area for the neural computational system in the test set was 0.825, whereas total PSA and complexed PSA alone had ROC areas of 0.678 and 0.697, respectively. The ROC area of logistic regression in the test set was 0.510, linear discriminant function analysis was 0.674, and quadratic discriminant function analysis was 0.011. All were significantly less than the ROC area of the neural computational model (all Ps < 0.002). Reverse regression based on Wilks's generalized likelihood ratio test demonstrated each input feature to be highly significant to the model (all Ps << 0.000001). CONCLUSIONS: The authors modeled a combination of well described patient risk factors for prostate carcinoma using a neural computational system with acceptable goodness of fit. They demonstrated that each of the seven variates on which the model was based was critically significant to model performance. The authors presented this model for clinical use and suggested that clinicians use it in deciding to perform prostate biopsy.
New approaches to the minimally invasive treatment of kidney tumors.
The incidence of renal cortical neoplasms has dramatically increased with the widespread use of abdominal imaging over the past 20 years. Coincidentally, the proportion of tumors that are smaller and incidentally detected has risen as well, indicative of a stage migration. The widespread application of minimally invasive and laparoscopic techniques to other organ systems has spurred the development of minimally invasive approaches to the management of renal tumors. The available data regarding laparoscopic nephrectomy, laparoscopic partial nephrectomy, and tissue ablative techniques, such as renal cryoablation, radiofrequency ablation, and high-intensity focused ultrasound are reviewed.