Decision-making and communication
Peter Hoskin, Peter Ostler in Clinical Oncology, 2020
Survival is the commonest end point for a large trial comparing two or more treatments for cancer. Whilst apparently straightforward in its definition time, cause of death may be difficult to trace, particularly in trials continuing for many years and where the condition has a long natural history for example patients in trials of prostate and breast cancer. It is important to define the cause of death. This will allow a comparison of not only overall survival but also disease-specific survival, i.e. counting only those patients dying from the disease under investigation. It is always important, however, to analyse all causes of death, since this may on occasions reveal an excess of deaths from the complications of the treatment. A typical example of this is the long-term analysis of the results of radiotherapy for breast cancer, where a reduction in breast cancer death rate is seen in patients receiving radiotherapy, but overall survival differences between those receiving radiotherapy and those who did not is less. The explanation for this apparent anomaly was explained by an excess of non-cancer deaths, predominantly cardiovascular disease in the radiotherapy group which partially negated the reduction in breast cancer deaths.
Prostate Segmentation from DW-MRI Using Level-Set Guided by Nonnegative Matrix Factorization
Ayman El-Baz, Gyan Pareek, Jasjit S. Suri in Prostate Cancer Imaging, 2018
Prostate cancer is one of the leading causes of cancer-related deaths in American men. Fortunately, the survival rate is relatively high for patients who are diagnosed with the disease in its early stages. Currently, in vivo imaging modalities have a major role in both the diagnosis and treatment of prostate cancer. The most common types of imaging modalities used in clinical diagnosis and treatment of prostate cancer are ultrasound and magnetic resonance imaging (MRI) [1–6]. The main strength of MRI over ultrasound is that MRI provides improved soft tissue contrast. Accurate prostate localization is an important step in diagnosis and treatment, such as developing computer-aided diagnostic (CAD) systems, guiding biopsy [7], and radiotherapy [8]. This accurate segmentation of the prostate can be done in a manual or an automated manner. Manual segmentation of the prostate is time-consuming, tiresome, and suffers from intra- and inter-observer variability [9]. As a result, developing automated and reliable prostate segmentation techniques from MRI is a clinically required task. The challenges related to this task include the following: the border between the prostate and its surrounding background is generally weak, the intensity distributions of the voxels around the border of the prostate vary across different subjects, and the shape of the prostate differs both across the different slices of the same subject and across different subjects [10].
Malignant Neoplasms of the Colon
Philip H. Gordon, Santhat Nivatvongs, Lee E. Smith, Scott Thorn Barrows, Carla Gunn, Gregory Blew, David Ehlert, Craig Kiefer, Kim Martens in Neoplasms of the Colon, Rectum, and Anus, 2007
Browne et al. (973) reported their experience with surgical resection for patients with locoregional recurrent colon carcinoma. A total of 744 patients with recurrent colon carcinoma were identified and 100 (13.4%) underwent exploration with curative intent for potentially resectable locoregional recurrence: 75 with isolated locoregional recurrence, and 25 with locoregional recurrence and resectable distant disease. The median follow-up for survivors was 27 months. Locoregional recurrence was classified into four categories: anastomotic; mesenteric/nodal; retroperitoneal; and peritoneal. Median survival for all patients was 30 months. Fifty-six patients had an R0 resection (including distant sites). Factors associated with prolonged disease-specific survival included R0 resection; age < 60 years; early stage of primary disease; and no associated distant disease. Poor prognostic factors included more than one site of recurrence and involvement of the mesenteric/nodal basin. The ability to obtain an R0 resection was the strongest predictor of outcome, and these patients had a median survival of 66 months.
Clinical Significance of Soluble Intercellular Adhesion Molecule-1 and Interleukin-6 in Patients with Extrahepatic Cholangiocarcinoma
Published in Journal of Investigative Surgery, 2018
Tatsuo Shimura, Masahiko Shibata, Kenji Gonda, Yasuhide Kofunato, Ryo Okada, Teruhide Ishigame, Takashi Kimura, Akira Kenjo, Shigeru Marubashi, Koji Kono, Seiichi Takenoshita
Data FIGURE 1 are presented as frequencies or percentages for categorical variables and mean ± SEM for continuous variables, unless otherwise indicated. For categorical clinical variables, the differences were evaluated by the χ2 test or Fisher's exact test where appropriate. The differences in mean values between the groups were analyzed using the Mann–Whitney U test. Associations between two variables were quantified using Spearman's rank correlation coefficient. The receiver operating characteristics (ROC) curve was used to evaluate the usefulness of the examined parameters as a prognostic factor. The mean time-to-event observation period was 36.2 months (range: 1.6−69.3). The final assessment of disease status was made on March 31, 2017. Disease-specific survival (DSS) was calculated using the Kaplan–Meier method and the differences were assessed by the log-rank test. Factors which were found to have a p-value of <.1 in the univariate analysis were subjected to multivariate analysis using a Cox proportional hazard model to identify independent predictors of prognosis. A two-sided p-value of <.05 was considered to indicate statistically significant differences. All statistical calculations were performed using SPSS® version 24 (IBM Japan, Tokyo, Japan). Not all blood samples were of sufficient volume for all measurements.
Clinicopathological indicators of survival among patients with pulmonary carcinoid tumor
Published in Acta Oncologica, 2018
Tiina Vesterinen, Sanna Mononen, Kaisa Salmenkivi, Harri Mustonen, Ilkka Ilonen, Aija Knuuttila, Caj Haglund, Johanna Arola
Differences in the dichotomous or nominal variables between the groups were calculated with the Fisher’s Exact Test. The cumulative survival probabilities were estimated with the Kaplan–Meier method, which was also used to graphically display the disease-specific survival (DSS) curves. The exact 95% confidence intervals (CI) were calculated for the survival rates. Differences in hazard rates (HR) by age, gender, histologic subtype, tumor size, presence of metastatic disease and immunohistochemical findings were tested with univariable Cox regression. If the regression did not converge, the Firth’s penalized Cox regression was calculated. Receiver operating characteristics (ROC) curves were used to estimate a cutoff value for Ki-67 as a prognostic indicator, accomplished by maximizing the Youden’s index (sensitivity + specificity −1). Survival was calculated from the date of the surgery to the last date of follow-up or death, and non-disease-specific deaths were censored to obtain DSS. A significant difference was predetermined to be a p value less than .05. Two-tailed tests were used. Analyses were performed using IBM SPSS Statistics 24 (IBM, Armonk, NY) and SAS for Windows 9.4 (SAS Institute Inc., Cary, NC).
Periodontal status at diagnosis predicts non-disease-specific survival in a geographically defined cohort of patients with oropharynx squamous cell carcinoma
Published in Acta Oto-Laryngologica, 2019
Mirna Farran, Sigbjørn Suk Løes, Olav Karsten Vintermyr, Stein Lybak, Hans Jørgen Aarstad
The statistical program package SPSS was employed (Ver. 24; SPSS Inc. Chicago, IL). A value of p < .05 was considered to indicate a statistically significant result. All p values reported represent two-sided tests. Correlations were studied by Pearson correlation coefficients. Analysis of variance (ANOVA) analyses were employed to study differences between HPV-negative and HPV-positive patients. The associations between the possible prognostic variables with survival were determined using Kaplan–Meier estimator and Cox proportional hazards regression models. The survival rate is reported as pertinent percentage survival and/or relative risk (RR) with confidence intervals. Non-disease-specific survival is reported as overall survival with disease-specific survival subtracted.
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