The disadvantage of visualizing tomography by slices is that an important attribute of the object, its volume, is not easily perceived or measured. In oncology this creates a problem, which is addressed here: if early...The disadvantage of visualizing tomography by slices is that an important attribute of the object, its volume, is not easily perceived or measured. In oncology this creates a problem, which is addressed here: if early detection and response to treatment are an important prognostic element, then volume is important. The literature has proposed surrogates to volume derived from measures on slices, but geometrically they are not well founded. Actual volume analysis is not complex, and the proposed method applies equally well to organs as to tumors. Volume based measures are more sensitive than individual SUV values, of which the commonly most used is the maximum Standardized Uptake Value (SUV<sub>m</sub>). If the tumor volume is defined, it can be replaced by the total tumor SUV (SUV<sub>t</sub>). If the metric for change is the ratio after/(before + after), in the patient population analyzed here, the SUV<sub>m</sub> metric averages 0.132 for response and 0.662 for progression, the total SUV<sub>t</sub> range is 0.069 to 0.734. In contrast to SUV<sub>t</sub>, SUV<sub>m</sub> is based on a weak sampling method since it is based on the value of a single voxel of more than 10 million.展开更多
The present paper is based on the observations that 1) there is reported variation in the specificities according to the type of tumor targeted (target) by FDG PET and 2) that while one can posit that the sensitivity ...The present paper is based on the observations that 1) there is reported variation in the specificities according to the type of tumor targeted (target) by FDG PET and 2) that while one can posit that the sensitivity of the tracer depends on the avidity for glucose and the plasma supply of the target, even so that the targeting cannot influence the avidity of unrelated tissues or lesions. The hypothesis to be tested is twofold: 1) patients imaged for different types of lesions could have a different prevalence of FDG avid tissues or lesions different from the target and 2) that the target lesions could be generally located in body location (sites) more likely to contain unrelated foci of increased uptake. Variance analysis shows that the sensitivity varies according to the target (p = 0.022), but not according to the location (p = 0.34);the specificity varies with the location (p = 0.0012) and the target (p = 0.05). Specificities are significantly different in different primary targets and target locations. The former is assumed to be due to different comorbidities in patients with different targets, the latter to the different locations of unrelated glucose avid organs or structures. Conclusion: When specificities are recorded or defined, the patient population characteristics and the organ or pathology of the false positives should also be described.展开更多
An observational finding found a large variation in the brain SUV in patients with multiple myeloma undergoing PET/CT. The first hypothesis considered a toxic effect of chemotherapeutic agents, but no correlation was ...An observational finding found a large variation in the brain SUV in patients with multiple myeloma undergoing PET/CT. The first hypothesis considered a toxic effect of chemotherapeutic agents, but no correlation was found with hematological signs of toxicity. Low brain FDG uptake has been described with anesthesia, but this was not relevant in this case. An alternative is the presence of a large FDG avid mass, but that was excluded. Since there was a question of chemotherapy toxicity, the metrics used for comparison were Hemoglobin levels (Hgb, g/dl), Erythrocyte count (RBC, M/μL), Lymphocytes absolute counts (Lymph#, K/μL) and % (lymph, %), Granulocytes Neutrophils, K/μL), age and C-reactive protein levels (CRP, g/L). The liver SUV (standardized uptake value) was included to eliminate unexpected global effects on the SUV values, since FDG uptake is a competitive system with a single source (plasma FDG levels). There was in fact no correlation between brain SUV and hepatic SUV, eliminating the so-called super scan effect. Further analysis, however, revealed a strong positive correlation with hemoglobin or RBC levels, but an inverse effect with Neutrophils, C-reactive proteins and age (in years). The results suggest that brain metabolism strongly depends on oxygen supply and may be depressed by general inflammatory diseases and independently with age. If the variation of glucose metabolism correlates with cognitive deficits (CD), considering general measures of good health may be a first step for relief of age related CD.展开更多
The validation of medical imaging (processing and acquisition) can be achieved in multiple ways, somewhat influenced by the context. There are three traps to avoid: First reliance on ground truth requires the knowledg...The validation of medical imaging (processing and acquisition) can be achieved in multiple ways, somewhat influenced by the context. There are three traps to avoid: First reliance on ground truth requires the knowledge of it before the end of the trial, second comparison to gold standards cannot show improvement and finally one needs to deal with confirmation bias. In this paper we discuss those topics and alternative validation schemes.展开更多
Purpose: Since HCC lesions are generally characterized by lower Hounsfield unit value (HU) values and higher tracer uptake (SUV or Standardized Uptake Values), we intended to determine if normalizing the SUV by the HU...Purpose: Since HCC lesions are generally characterized by lower Hounsfield unit value (HU) values and higher tracer uptake (SUV or Standardized Uptake Values), we intended to determine if normalizing the SUV by the HU, for the lesion and normal liver would improve sensitivity and specificity. Material and Methods: Twenty-three consecutive patients with HCC diagnosed clinically or pathologically underwent C11-Acetate (C11-A) and F18-FDG (FDG) PET/CT imaging before surgery during a 424-day interval. After exclusion of treated or calcified lesions, 44 lesions are included in this study. The original metrics are the maximum SUV (SUVmax) and maximum or average HU (HUmax or HUmean) for lesions and normal liver. For the normal liver, an average SUV (SUVmean) was included. The derived values are the ratios of SUV/HU values. The efficacy is the fraction of outcomes of non-overlapping metrics between lesion and normal liver. Results: For FDG the efficacy is 0.489 for the lesions SUVmax versus normal liver SUVmax. For lesion SUVmax/HUmean versus normal liver SUVmax/HUmax, the efficacy is 1.00. For C11-A the corresponding values are 0.045 and 0.920. Conclusion: Normalizing SUV values for changes in HU values increases the contrast between normal liver and lesions. Analytical fusion can be very effective.展开更多
文摘The disadvantage of visualizing tomography by slices is that an important attribute of the object, its volume, is not easily perceived or measured. In oncology this creates a problem, which is addressed here: if early detection and response to treatment are an important prognostic element, then volume is important. The literature has proposed surrogates to volume derived from measures on slices, but geometrically they are not well founded. Actual volume analysis is not complex, and the proposed method applies equally well to organs as to tumors. Volume based measures are more sensitive than individual SUV values, of which the commonly most used is the maximum Standardized Uptake Value (SUV<sub>m</sub>). If the tumor volume is defined, it can be replaced by the total tumor SUV (SUV<sub>t</sub>). If the metric for change is the ratio after/(before + after), in the patient population analyzed here, the SUV<sub>m</sub> metric averages 0.132 for response and 0.662 for progression, the total SUV<sub>t</sub> range is 0.069 to 0.734. In contrast to SUV<sub>t</sub>, SUV<sub>m</sub> is based on a weak sampling method since it is based on the value of a single voxel of more than 10 million.
文摘The present paper is based on the observations that 1) there is reported variation in the specificities according to the type of tumor targeted (target) by FDG PET and 2) that while one can posit that the sensitivity of the tracer depends on the avidity for glucose and the plasma supply of the target, even so that the targeting cannot influence the avidity of unrelated tissues or lesions. The hypothesis to be tested is twofold: 1) patients imaged for different types of lesions could have a different prevalence of FDG avid tissues or lesions different from the target and 2) that the target lesions could be generally located in body location (sites) more likely to contain unrelated foci of increased uptake. Variance analysis shows that the sensitivity varies according to the target (p = 0.022), but not according to the location (p = 0.34);the specificity varies with the location (p = 0.0012) and the target (p = 0.05). Specificities are significantly different in different primary targets and target locations. The former is assumed to be due to different comorbidities in patients with different targets, the latter to the different locations of unrelated glucose avid organs or structures. Conclusion: When specificities are recorded or defined, the patient population characteristics and the organ or pathology of the false positives should also be described.
文摘An observational finding found a large variation in the brain SUV in patients with multiple myeloma undergoing PET/CT. The first hypothesis considered a toxic effect of chemotherapeutic agents, but no correlation was found with hematological signs of toxicity. Low brain FDG uptake has been described with anesthesia, but this was not relevant in this case. An alternative is the presence of a large FDG avid mass, but that was excluded. Since there was a question of chemotherapy toxicity, the metrics used for comparison were Hemoglobin levels (Hgb, g/dl), Erythrocyte count (RBC, M/μL), Lymphocytes absolute counts (Lymph#, K/μL) and % (lymph, %), Granulocytes Neutrophils, K/μL), age and C-reactive protein levels (CRP, g/L). The liver SUV (standardized uptake value) was included to eliminate unexpected global effects on the SUV values, since FDG uptake is a competitive system with a single source (plasma FDG levels). There was in fact no correlation between brain SUV and hepatic SUV, eliminating the so-called super scan effect. Further analysis, however, revealed a strong positive correlation with hemoglobin or RBC levels, but an inverse effect with Neutrophils, C-reactive proteins and age (in years). The results suggest that brain metabolism strongly depends on oxygen supply and may be depressed by general inflammatory diseases and independently with age. If the variation of glucose metabolism correlates with cognitive deficits (CD), considering general measures of good health may be a first step for relief of age related CD.
文摘The validation of medical imaging (processing and acquisition) can be achieved in multiple ways, somewhat influenced by the context. There are three traps to avoid: First reliance on ground truth requires the knowledge of it before the end of the trial, second comparison to gold standards cannot show improvement and finally one needs to deal with confirmation bias. In this paper we discuss those topics and alternative validation schemes.
文摘Purpose: Since HCC lesions are generally characterized by lower Hounsfield unit value (HU) values and higher tracer uptake (SUV or Standardized Uptake Values), we intended to determine if normalizing the SUV by the HU, for the lesion and normal liver would improve sensitivity and specificity. Material and Methods: Twenty-three consecutive patients with HCC diagnosed clinically or pathologically underwent C11-Acetate (C11-A) and F18-FDG (FDG) PET/CT imaging before surgery during a 424-day interval. After exclusion of treated or calcified lesions, 44 lesions are included in this study. The original metrics are the maximum SUV (SUVmax) and maximum or average HU (HUmax or HUmean) for lesions and normal liver. For the normal liver, an average SUV (SUVmean) was included. The derived values are the ratios of SUV/HU values. The efficacy is the fraction of outcomes of non-overlapping metrics between lesion and normal liver. Results: For FDG the efficacy is 0.489 for the lesions SUVmax versus normal liver SUVmax. For lesion SUVmax/HUmean versus normal liver SUVmax/HUmax, the efficacy is 1.00. For C11-A the corresponding values are 0.045 and 0.920. Conclusion: Normalizing SUV values for changes in HU values increases the contrast between normal liver and lesions. Analytical fusion can be very effective.
