Guidelines for time-to-event end point definitions in sarcomas and gastrointestinal stromal tumors (GIST) trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials)†.

BACKGROUND: The use of potential surrogate end points for overall survival, such as disease-free survival (DFS) or time-to-treatment failure (TTF) is increasingly common in randomized controlled trials (RCTs) in cancer. However, the definition of time-to-event (TTE) end points is rarely precise and lacks uniformity across trials. End point definition can impact trial results by affecting estimation of treatment effect and statistical power. The DATECAN initiative (Definition for the Assessment of Time-to-event End points in CANcer trials) aims to provide recommendations for definitions of TTE end points. We report guidelines for RCT in sarcomas and gastrointestinal stromal tumors (GIST). METHODS: We first carried out a literature review to identify TTE end points (primary or secondary) reported in publications of RCT. An international multidisciplinary panel of experts proposed recommendations for the definitions of these end points. Recommendations were developed through a validated consensus method formalizing the degree of agreement among experts. RESULTS: Recommended guidelines for the definition of TTE end points commonly used in RCT for sarcomas and GIST are provided for adjuvant and metastatic settings, including DFS, TTF, time to progression and others. CONCLUSION: Use of standardized definitions should facilitate comparison of trials' results, and improve the quality of trial design and reporting. These guidelines could be of particular interest to research scientists involved in the design, conduct, reporting or assessment of RCT such as investigators, statisticians, reviewers, editors or regulatory authorities.

Correlation between radiological assessment and histopathological diagnosis in retroperitoneal tumors: analysis of 291 consecutive patients at a tertiary reference sarcoma center.

OBJECTIVES: Aim of study was to assess the correlation between computed tomography scan (CT) findings and histopathology. MATERIAL AND METHODS: Data were collected on consecutive patients with suspected retroperitoneal sarcoma (RPS) referred to a tertiary sarcoma center. Patients underwent contrast enhanced multi-detector CT scans. Radiological features of lesions were classified according to the presence of a fatty (Group A) mass, or non-fatty (Group B) mass, both subdivided according to homogeneity and intralesional high-contrasted appearance. Radiological classification was compared with histopathological diagnosis. Sensitivity, specificity, positive/negative predictive value (PPV, NPV) were analyzed. RESULTS: Of 291 patients, 103/291 (35.4%) masses were classified in Group A and 188/291 (64.6%) in Group B. Diagnosis of mesenchymal tumor was obtained in 231/291 cases (79%) and non-mesenchymal tumor in 60/291 (21%). Sensitivity and specificity of Group A for liposarcoma were 76.7% and 92.0%; PPV and NPV were 86.4% and 85.6%. Sensitivity of Group B for a mesenchymal tumor was 55.4% and specificity was 0%; PPV and NPV were 68.1% and 0%. CONCLUSIONS: None of radiological criteria were sufficient to anticipate a specific diagnosis, with the only exception of well differentiated liposarcoma and angiomyolipoma. In a series of suspected RPS, 21% of the lesions were finally non-mesenchymal tumors.

Growth modulation index as metric of clinical benefit assessment among advanced soft tissue sarcoma patients receiving trabectedin as a salvage therapy.

BACKGROUND: The growth modulation index (GMI) is the ratio of time to progression with the nth line (TTP(n)) of therapy to the TTP(n)(-1) with the n-1th line. GMI >1.33 is considered as a sign of activity in phase II trials. PATIENTS AND METHODS: This retrospective analysis evaluated the concordance between the GMI and the efficacy outcomes in 279 patients with advanced soft tissue sarcoma (ASTS) treated with trabectedin 1.5 mg/m² (24-h infusion every 3 weeks) in four phase II trials. RESULTS: One hundred and forty-two (51%) patients received one prior line and 137 ≥ 2 lines. The median TTP(n) was 2.8 months (range 0.2-26.8), whereas the median TTP(n)(-1) was 4.0 months (0.3-79.5). The median GMI was 0.6 (0.0-14.4). Overall, 177 patients (63%) had a GMI <1; 21 (8%) a GMI equal to 1-1.33 and 81 (29%) a GMI >1.33, which correlated with the median overall survival in those patients (9.1, 13.9 and 23.8 months, respectively, P = 0.0005). A high concordance rate between the GMI and response rate (P < 0.0001) and progression-free survival (PFS, P < 0.0001) was observed. Good performance status (PS) was the only factor associated with GMI >1.33 (PS = 0; P < 0.04). CONCLUSIONS: A high GMI was associated with favorable efficacy outcomes in patients treated with trabectedin. Further research is needed to assess GMI as an indicator in this setting.

Outcome assessment in cancer management.

The goals of cancer treatment may be prevention, cure, or palliation. In each case, the length and quality of survival are the ultimate outcomes to assess when evaluating the success of treatment. Some surrogate or intermediate endpoints, however, may be considered, such as toxicity or the tumor response. The first section of this article considers traditional outcomes and endpoints and intermediate or surrogate endpoints for survival and quality of life. The second section specifically addresses the issue of the quality-of-life outcome, which has become increasingly important over the last 2 decades. The last section suggests that combining survival and quality of life may not be easy. Costs are increasingly important in management of diseases, and the outcomes of cancer treatment frequently are measured against them. Although the definitions of cost-effectiveness and cost-utility are mentioned at the end of this article, the article does not deal with the problems of decision making for health resource allocation.

Quality of life assessment and clinical decision-making.

Survival and quality of life are the two end-points of cancer treatment. Recommendations are available as to how quality of life should be assessed in clinical studies in order for quantitative scores to be provided for it, along with survival data. The two measurements can be combined, and their total should yield a definition of the state of the art in cancer treatment with respect to both the quantity and the quality of life. However, at the individual level, quality of life can be traded off against survival only by taking into account the patient's attitude toward risk. More importantly, the quality of health status may be valued in a completely different way from patient to patient. Thus, when a clinical choice is to be made from among different treatment options, the ethical principle of respect for the patient's autonomy would require that the patient be informed of their possible respective outcomes, and allowed to provide his/her own assessment of the quality of life associated with these outcomes. This might have consequences for quality of life assessment in clinical studies, which should not only quantify average scores for treatment comparisons, but also provide a 'health state description' with respect to the aftermath of each of the treatment options. These data could be formalized in the already proposed decision instruments incorporating clinical scenarios for patient information. In any case, this should allow individualized clinical decisions incorporating each patient's preferences for the quality of his/her expected life span. Providing average quality of life scores may be useful, indeed, for population-based health decisions, as are those on resource allocation and those on registration of new drugs.