Validation of a Predictive Model for Survival in Patients With Advanced Cancer: Secondary Analysis of RTOG 9714.

BackgroundThe objective of this study was to validate a simple predictive model for survival of patients with advanced cancer.MethodsPrevious studies with training and validation datasets developed a model predicting survival of patients referred for palliative radiotherapy using three readily available factors: primary cancer site, site of metastases and Karnofsky performance score (KPS). This predictive model was used in the current study, where each factor was assigned a value proportional to its prognostic weight and the sum of the weighted scores for each patient was survival prediction score (SPS). Patients were also classified according to their number of risk factors (NRF). Three risk groups were established. The Radiation Therapy and Oncology Group (RTOG) 9714 data was used to provide an additional external validation set comprised of patients treated among multiple institutions with appropriate statistical tests.ResultsThe RTOG external validation set comprised of 908 patients treated at 66 different radiation facilities from 1998 to 2002. The SPS method classified all patients into the low-risk group. Based on the NRF, two distinct risk groups with significantly different survival estimates were identified. The ability to predict survival was similar to that of the training and previous validation datasets for both the SPS and NRF methods.ConclusionsThe three variable NRF model is preferred because of its relative simplicity

Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging

To dynamically detect and characterize 18F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue

Manipulation of Microenvironment with a Built-in Electrochemical Actuator in Proximity of a Dissolved Oxygen Microsensor

Abstract - Biochemical sensors for continuous monitoring require dependable periodic self- diagnosis with acceptable simplicity to check its functionality during operation. An in situ self- diagnostic technique for a dissolved oxygen microsensor is proposed in an effort to devise an intelligent microsensor system with an integrated electrochemical actuation electrode. With a built- in platinum microelectrode that surrounds the microsensor, two kinds of microenvironments, called the oxygen-saturated or oxygen-depleted phases, can be created by water electrolysis depending on the polarity. The functionality of the microsensor can be checked during these microenvironment phases. The polarographic oxygen microsensor is fabricated on a flexible polyimide substrate (Kapton) and the feasibility of the proposed concept is demonstrated in a physiological solution. The sensor responds properly during the oxygen-generating and oxygen- depleting phases. The use of these microenvironments for in situ self-calibration is discussed to achieve functional integration as well as structural integration of the microsensor system

Validation of a Predictive Model for Survival in Patients With Advanced Cancer: Secondary Analysis of RTOG 9714.

BackgroundThe objective of this study was to validate a simple predictive model for survival of patients with advanced cancer.MethodsPrevious studies with training and validation datasets developed a model predicting survival of patients referred for palliative radiotherapy using three readily available factors: primary cancer site, site of metastases and Karnofsky performance score (KPS). This predictive model was used in the current study, where each factor was assigned a value proportional to its prognostic weight and the sum of the weighted scores for each patient was survival prediction score (SPS). Patients were also classified according to their number of risk factors (NRF). Three risk groups were established. The Radiation Therapy and Oncology Group (RTOG) 9714 data was used to provide an additional external validation set comprised of patients treated among multiple institutions with appropriate statistical tests.ResultsThe RTOG external validation set comprised of 908 patients treated at 66 different radiation facilities from 1998 to 2002. The SPS method classified all patients into the low-risk group. Based on the NRF, two distinct risk groups with significantly different survival estimates were identified. The ability to predict survival was similar to that of the training and previous validation datasets for both the SPS and NRF methods.ConclusionsThe three variable NRF model is preferred because of its relative simplicity

Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging

PURPOSE: To dynamically detect and characterize (18)F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue. METHODS: Investigational gamma scintillation sensors were topically applied to patients with locally advanced breast cancer scheduled to undergo limited whole-body FDG-PET as part of an ongoing clinical study. Relative to the affected breast, sensors were placed on the contralateral injection arm and ipsilateral control arm during the resting uptake phase prior to each patient’s PET scan. Time activity curves (TACs) from the sensors were integrated at varying intervals (0–10, 0–20, 0–30, 0–40, and 30–40 min) post-FDG and the resulting areas-under-the-curve (AUCs) were compared to SUVs obtained from PET. RESULTS: In cases of infiltration, observed in three sensor recordings (30%), the injection arm TAC shape varied depending on the extent and severity of infiltration. In two of these cases TAC characteristics suggested the infiltration was partially resolving prior to image acquisition, although it was still apparent on subsequent PET. Areas under the TAC 0–10 and 0–20 min post-FDG were significantly different in infiltrated versus non-infiltrated cases (Mann-Whitney, p < 0.05). When normalized to control, all TAC integration intervals from the injection arm were significantly correlated with SUV(peak) and SUV(max) measured over the infiltration site (Spearman ρ ≥ 0.77, p < 0.05). Receiver operating characteristic (ROC) analyses, testing the ability of the first 10 minutes of post-FDG sensor data to predict infiltration visibility on the ensuing PET, yielded an area under the ROC curve of 0.92. CONCLUSION: Topical sensors applied near the injection site provide dynamic information from the time of FDG administration through the uptake period and may be useful in detecting infiltrations regardless of PET image field of view. This dynamic information may also complement the static PET image to better characterize the true extent of infiltrations