Development and validation of a bedside score to predict early death in cancer of unknown primary patients

Relationship between Survival Days, Cancer Cachexia, and Activities of Daily Living in Palliative Cancer Patients Undergoing Rehabilitation

Objectives

Cancer cachexia has many effects on physical function and causes a decline in activities of daily living (ADL). Therefore, rehabilitation programs should be structured according to the degree of cancer cachexia. Currently, the evaluation of cancer cachexia is mainly based on body mass. However, there is no report on the use of the modified Glasgow Prognostic Score (mGPS) to evaluate the degree of cancer cachexia and survival prognosis in palliative cancer patients for whom rehabilitation has been prescribed. This study used mGPS to examine the prevalence of cancer cachexia in palliative cancer patients undergoing rehabilitation and the impacts of cancer cachexia, ADL, and complications on survival.

Methods

The participants included 135 palliative cancer patients who were admitted to the hospital and underwent rehabilitation between 2020 and 2022. Cancer cachexia classification by mGPS was conducted, and logistic regression analysis was used to examine factors affecting the survival of palliative cancer patients undergoing rehabilitation.

Results

The patients were grouped as follows: 6 (4.4%) normal, 13 (9.6%) undernourished, 12 (9.0%) pre-cachexia, and 104 (77.0%) refractory cachexia. Logistic regression analysis showed that the mGPS and BI affected survival.

Conclusions

In a cohort of palliative cancer patients undergoing rehabilitation, 86% had cachexia. mGPS and BI were associated with survival outcomes. Combination of mGPS classification with ADL assessment may provide meaningful prognostic information in these patients.

SEOM-GECOD clinical guideline for unknown primary cancer (2021)

Cancer of unknown primary site (CUP) is defined as a heterogeneous group of tumors that appear as metastases, and of which standard diagnostic work-up fails to identify the origin. It is considered a separate entity with a specific biology, and nowadays molecular characteristics and the determination of actionable mutations may be important in a significant group of patients. In this guide, we summarize the diagnostic, therapeutic, and possible new developments in molecular medicine that may help us in the management of this unique disease entity.

Extensive diagnostic work-up for patients with carcinoma of unknown primary

Patients with carcinoma of unknown primary (CUP) present with metastatic disease without an identified primary tumour. The unknown site of origin makes the diagnostic work-up and treatment challenging. Since little information is available regarding diagnostic work-up and treatment in daily practice, we collected and analysed these in a patient cohort with regard to the recommendations of the national CUP guideline. Data of 161 patients diagnosed with CUP in 2014 or 2015 were extracted from the Netherlands Cancer Registry (NCR) and supplemented with diagnostic work-up information from patient files and analysed. Patients underwent an average of five imaging studies during the diagnostic phase (range 1-17). From the tests as recommended in the national guideline on CUP, a chest X-ray was most commonly performed (73%), whereas a PET-CT was done in one out of four patients (24%). Biopsies were taken in 86% of the study population, with Cytokeratin 7 being the most frequently tested histopathological marker (73%). Less than half of patients received therapy (42%). CUP patients undergo extensive diagnostic work-up. The performance status did not influence the extent of the diagnostic work-up in CUP patients, but it was an important factor for receiving treatment.

A biobank analysis of prognostic biomarkers of the systemic inflammatory response in patients presenting with malignancy of undefined primary origin

BACKGROUND

Survival prediction in patients presenting with malignancy of undefined primary origin (MUO) is challenging, with a lack of validated prognostic tools. Biomarkers of the systemic inflammatory response independently predict survival in other cancer types, but their role in MUO is unclear. The aim of this study was to assess biomarkers of the systemic inflammatory response in patients presenting with MUO.

PATIENTS AND METHODS

A biobank of 1049 patients presenting with MUO referred to a regional oncology service in Scotland was analysed. Key inflammatory biomarkers (white cell count, neutrophil count and C-reactive protein combined with albumin [to give the modified Glasgow Prognostic Score {mGPS}]) were examined. The relationship between these and survival was examined using Kaplan-Meier and Cox regression methods.

RESULTS

Data were available for 1049 patients. Median survival was 4.3 months (interquartile range: 1.7-16.0 months). On multivariate analysis mGPS was independently associated with survival and stratified survival from 13.6 months (mGPS: 0) to 2.3 months (mGPS: 2) (p < 0.001). The mGPS was predictive of survival on multivariate analysis in patients found to have a non-cancer diagnosis (p = 0.034), an identified primary cancer (0.002), cancer of unknown primary (CUP) (p = 0.011), those for whom biopsy was not done (MUO) (p = 0.036), those found to have an identified primary cancer (0.002) and even those found to have a non-cancer diagnosis (p = 0.034) after further detailed investigations. In patients with CUP mGPS predicted survival regardless of the recognised clinicopathological prognostic subgroup (p < 0.001).

CONCLUSIONS

The results of the present study demonstrate that biomarkers of the systemic inflammatory response are reliable prognostic factors in patients presenting with MUO. These simple, objective, routine clinical tests may inform clinical management.

Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

Background

We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data.

Methods

Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (₄₀C₃ = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings.

Results

A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1–6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models.

Conclusion

By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy.

First-Line Chemotherapy for Metastatic Esophageal Squamous Cell Carcinoma: Clinico-Biological Predictors of Disease Control

OBJECTIVE

This study aimed to identify predictors of tumor control (TC) in metastatic esophageal squamous cell carcinoma patients receiving first-line chemotherapy.

METHODS

A development cohort of 68 patients from a prospective multicenter trial (NCT01248299) was used to identify predictors of TC at first radiological tumor assessment and to generate a predictive score for TC. That score was applied in an independent retrospective single-center validation cohort of 60 consecutive patients.

RESULTS

Multivariate analysis identified three predictors of TC: body mass index ≥18.5 (OR 4.5, 95% CI 0.91-22.5), absence of bone metastasis (OR 4.6, 95% CI 0.91-23.2) and albumin ≥35 g/l (OR 3.5, 95% CI 1.0-12.1). Based on the presence or absence of these three independent prognosticators, we built a predictive model using a score from 0 to 3. In the development cohort, the TC rates were 14.3 and 78.0% and in the validation cohort 12.5 and 44.2%, for scores of 0-1 and 2-3, respectively. With negative predictive values of 85 and 88% in the development and validation cohorts, respectively, we were able to identify patients with a very low probability of TC.

CONCLUSION

We have developed and validated a score that can be easily determined at the bedside to predict TC in metastatic esophageal squamous cell carcinoma patients.

A Simple Risk Model to Predict Survival in Patients With Carcinoma of Unknown Primary Origin

Carcinoma of unknown primary origin (CUP) is characterized by diverse histological subtypes and clinical presentations, ranging from clinically indolent to frankly aggressive behaviors. This study aimed to identify prognostic factors of CUP and to develop a simple risk model to predict survival in a cohort of Asian patients.We retrospectively reviewed 190 patients diagnosed with CUP between 2007 and 2012 at a single medical center in Taiwan. The clinicopathological parameters and outcomes of our cohort were analyzed. A risk model was developed using multivariate logistic regression and a prognostic score was generated.The prognostic score was calculated based on 3 independent prognostic variables: the Eastern Cooperative Oncology Group (ECOG) scale (0 points if the score was 1, 2 points if it was 2-4), visceral organ involvement (0 points if no involvement, 1 point if involved), and the neutrophil-to-lymphocyte ratio (0 points if ≤3, 1 point if >3). Patients were stratified into good (score 0), intermediate (score 1-2), and poor (score 3-4) prognostic groups based on the risk model. The median survival (95% confidence interval) was 1086 days (500-1617, n = 42), 305 days (237-372, n = 75), and 64 days (44-84, n = 73) for the good, intermediate, and poor prognostic groups, respectively. The c-statistics using the risk model and ECOG scale for the outcome of 1-year mortality were 0.80 and 0.70 (P = 0.038), respectively.In this study, we developed a simple risk model that accurately predicted survival in patients with CUP. This scoring system may be used to help patients and clinicians determine appropriate treatments.

Inflammation as a validated prognostic determinant in carcinoma of unknown primary site

Background:

Carcinoma of unknown primary (CUP) is a clinical presentation with a poor prognosis. Inflammation-based prognostic systems are stage-independent prognostic predictors in various malignancies. We aimed to assess the accuracy of the modified Glasgow Prognostic Score (mGPS), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) as objective prognostic models in CUP.

Methods:

We derived inflammatory scores in 60 consecutive CUP referrals to the Imperial College oncology unit between 1996 and 2011. Patient demographics, treatment and staging data and full blood profiles were collected. An independent cohort of 179 patients presenting to the Taipei Veterens Hospital between 2000 and 2009 were used as a ‘validation' data set. Uni- and multivariate survival analysis was used to predict the overall survival (OS).

Results:

Sixty patients were included: median age 61 (range: 33–86); 51% men; median OS 5.9 months (0.7–42.9); 88% with distant metastases. On univariate analysis NLR >5 (P=0.04) and mGPS (score 1–2) (P=0.03) correlated with OS. Multivariate analysis demonstrated significant hazard ratios for NLR; 2.02 (CI 1.0–4.1) (P=0.04) and mGPS; 1.52 (CI 1.0–2.3) (P=0.03). These findings were reinforced by analysis of the validation data.

Conclusion:

NLR and mGPS are independent, externally validated prognostic markers in CUP, with superior objectivity compared with performance status.

Performance status is the most powerful risk factor for early death among patients with advanced soft tissue sarcoma: the European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (STBSG) and French Sarcoma Group (FSG) study

BACKGROUND

We investigated prognostic factors (PFs) for 90-day mortality in a large cohort of advanced/metastatic soft tissue sarcoma (STS) patients treated with first-line chemotherapy.

METHODS

The PFs were identified by both logistic regression analysis and probability tree analysis in patients captured in the Soft Tissue and Bone Sarcoma Group (STBSG) database (3002 patients). Scores derived from the logistic regression analysis and algorithms derived from probability tree analysis were subsequently validated in an independent study cohort from the French Sarcoma Group (FSG) database (404 patients).

RESULTS

The 90-day mortality rate was 8.6 and 4.5% in both cohorts. The logistic regression analysis retained performance status (PS; odds ratio (OR)=3.83 if PS=1, OR=12.00 if PS ≥2), presence of liver metastasis (OR=2.37) and rare site metastasis (OR=2.00) as PFs for early death. The CHAID analysis retained PS as a major discriminator followed by histological grade (only for patients with PS ≥2). In both models, PS was the most powerful PF for 90-day mortality.

CONCLUSION

Performance status has to be taken into account in the design of further clinical trials and is one of the most important parameters to guide patient management. For those patients with poor PS, expected benefits from therapy should be weighed up carefully against the anticipated toxicities.