Morphometry of 31 pancreatic islet cell tumors was examined to determine the value of this technique in assessing tumor behavior. Patients were followed for a mean period of 5.1 years (range, 1 month-14 years) after diagnosis. Initially 17 localized and nine metastatic tumors were studied. Discriminant analysis was carried out on these cases and identified nuclear/cytoplasmic ratio and number of nuclei/mm2 as the significant discriminatory features. These were combined to derive a classification rule which was capable of correctly identifying localized and metastatic tumors in 92% of cases. The classification rule was applied subsequently to an additional five test cases, all of which were classified successfully. The failure of increased nuclear size and pleomorphism to correlate with malignancy in these tumors was confirmed. Tumors which metastasized had significantly greater gross diameters than localized lesions, but overlap existed. Mitotic counts were not a helpful discriminatory feature. Morphometry may be useful in improving histologic assessment of pancreatic islet cell tumor behavior.
Background Previous studies have established that higher baseline quality of life (QOL) scores are associated with improved survival in patients with metastatic colorectal cancer (mCRC). We examined the relationship between overall survival (OS) and baseline QOL. Patients and Methods A total of 1 247 patients with mCRC participating in N9741 (comparing bolus 5-FU/LV, irinotecan [IFL] vs infusional 5-FU/leucovorin [LV]/oxaliplatin [FOLFOX] vs. irinotecan/oxaliplatin [IROX]) provided data at baseline on overall QOL using a single-item linear analogue self-assessment (LASA) 0–100 point scale. The association of OS according to clinically deficient (defined as CD-QOL, score 0–50) vs not clinically deficient (nCD-QOL, score 51–100) baseline QOL scores was tested. A multivariable analysis using Cox proportional hazards modeling was performed to adjust for the effects of multiple baseline factors. An exploratory analysis was performed evaluating OS according to baseline QOL status among patients who did or did not receive second-line therapy. Results Baseline QOL was a strong predictor of OS for the whole cohort (CD-QOL vs nCD-QOL: 11.2 months vs 18.4 months, P < .0001), and in each arm IFL 12.4 vs 15.1 months, FOLFOX 11.1 months vs 20.6 months, and IROX 8.9 months vs 18.1 months. Baseline QOL was associated with baseline performance status (PS) ( P < .0001). After adjusting for PS and treatment arm, baseline QOL was still associated with OS ( P = .017). Conclusions Baseline QOL is an independent prognostic factor for OS in patients with mCRC. The demonstration that patient-assessed QOL and PS are independent prognostic indicators suggests that these assessments provide important complementary prognostic information.
For primary lung cancer, it is crucial to identify new predictors to guide the development of new strategies for predicting and improving survival. A multivariable study was conducted comparing patients who survived >5 years to whom died <2 years after diagnosis, matched on age, gender, TNM stage, tumor number and cell type. Patients were compared on other characteristics at diagnosis, follow-up information and genotypes on 5 critical enzymes in the glutathione metabolic pathway. Multiple logistic regression analysis evaluated all variables encompassing 4 dimensions. Included were 394 pairs of patients. Univariable analysis showed that smoking status and pack-years smoked, tumor grade, disease progression/recurrence, pulmonary resection and surgery type and selected comorbidities were significantly associated with survival. Patients who were physically active, reported a better quality of life after their diagnosis, or had positive GSTM1 genotype experienced longer survival. In multivariable analysis, disease progression/recurrence (OR: 3. Physicians often consider the characteristics of a diagnostic test as a discrete set of parameters generally falling into two categories of validity and reliability. Such interpretation of the test result is not an optimal way of harnessing all available information. A model is proposed to combine all measures of a test into a framework inspired from the Signal Detection Theory (SDT). By simulating subject and observer variation, the model is capable of transforming the measures of validity and reliability into the SDT parameters and vice versa. Two implications follow: First, the ideal performance of the test (when observer variation is eliminated) can be estimated. Second: quantitative interpretation of repeated tests, a common clinical scenario, is possible. Modeling results of a published study on interpretation of mammography for detecting breast cancer (Kerlikowske 1998) further clarifies such implications. The original study reported the sensitivity, specificity, intra-observer, and inter-observer disagreement of the test as 0.72, 0.83, 0.14, and 0.22, respectively. The model shows that by eliminating observer variation, the sensitivity and specificity will increase to 0.86 and 0.92, respectively. The combined sensitivity and specificity of two radiologists' reports the same mammogram (considering the test as positive when at least one radiologist reports a positive result) will be 0.87 and 0.72, respectively. A multivariate probabilistic sensitivity analysis was also performed and the model was found to be robust against minor changes in the inputs. Such an integrative approach can help obtain additional information beyond what is obtainable from the conventional interpretation of test results. The use of smoking cessation aids, such as nicotine replacement therapy, bupropion, and other substances designed to reduce cravings and withdrawal symptoms, is considered a promising approach, and thousands of articles report on such interventions. Roughly one fifth of those ar...
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