Disease volumes as a marker for patient response in malignant pleural mesothelioma

Our plain-English summary of Disease volumes as a marker for patient response in malignant pleural mesothelioma, by Z.E. Labby, A.K. Nowak, J.J. Dignam, et al (Annals of Oncology, 2013).

The study

• Authors: Z.E. Labby, A.K. Nowak, J.J. Dignam, C. Straus, H.L. Kindler, S.G. Armato
• Journal: Annals of Oncology
• Published: 2013;24(4):999-1005
• Cited by: 29 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Background: The goal of this study was to create a comprehensive model for malignant pleural mesothelioma patient survival utilizing continuous, time-varying estimates of disease volume from computed tomography (CT) imaging in conjunction with clinical covariates.

Patients And Methods: Serial CT scans were obtained during the course of clinically standard chemotherapy for 81 patients. The pleural disease volume was segmented for each of the 281 CT scans, and relative changes in disease volume from the baseline scan were tracked over the course of serial follow-up imaging. A prognostic model was built using time-varying disease volume measurements in conjunction with clinical covariates.

Results: Over the course of treatment, disease volume decreased by an average of 19%, and median patient survival was 12.6 months from baseline. In a multivariate survival model, changes in disease volume were significantly associated with patient survival along with disease histology, Eastern Cooperative Oncology Group performance status, and presence of dyspnea.

Conclusions: Analysis of the trajectories of disease volumes during chemotherapy for patients with mesothelioma indicates that increasing disease volume was significantly and independently associated with poor patient prognosis in both univariate and multivariate survival models.

In plain English

The authors used CT scans of 81 patients with malignant pleural mesothelioma, enrolled late 2003 to 2010 in an earlier prospective study in Western Australia. Patients received standard combination chemotherapy. A semiautomated method measured pleural disease volume on all 281 scans. By the end of treatment, volume had fallen by a geometric mean of 19%. Median survival from the baseline scan was 12.6 months (2.5 to 97.5 percentiles, 10.6 to 14.9 months), and 76 of 81 patients died. In the final combined model, the continuous rate of volume change from baseline (the 'specific growth rate') had a hazard ratio of 1.31 (95% confidence interval 1.13 to 1.53). Faster growth went with shorter survival. Non-epithelioid tumor type, shortness of breath and poorer performance status were also associated with shorter survival. The authors concluded that these disease volumes were prognostically significant. They noted that measured volume may include pleural effusion as well as tumor, and that the model needs validation in a larger independent group. Known asbestos exposure (75 of 81 patients) was not among the reported survival predictors.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Analysis of imaging and clinical data previously collected in a prospective imaging study, used to build and internally validate a survival (prognostic) model
• Who was studied: 81 adults with histologically or cytologically confirmed malignant pleural mesothelioma and no prior chemotherapy or radiotherapy, enrolled late 2003 to 2010 under ethics approval at Sir Charles Gairdner Hospital, Perth, Western Australia. There were 68 men and 13 women, with a median age at diagnosis of 66 (range 41 to 80). There were 281 CT scans in total.
• Exposure: Not an asbestos exposure study. The factor studied was change in pleural disease volume on serial CT scans during combination chemotherapy (cisplatin or carboplatin with gemcitabine or pemetrexed). Known asbestos exposure was recorded as yes for 75 patients and no for 6. No fiber types are given.
• Main results:
  • Survival was measured from the pretreatment baseline scan. Median survival for the whole cohort of 81 patients was 12.6 months (2.5 to 97.5 percentiles 10.6 to 14.9 months; range 1.7 to 60 months). There were 76 observed deaths.
  • Mean pleural disease volume at baseline was 1511 plus or minus 1065 ml (range 225 to 5287 ml). The geometric mean change from baseline was minus 11% at the first follow-up scan and minus 19% by the end of treatment.
  • In single-factor (univariate) analysis, disease volume modeled as the continuous, time-varying specific growth rate from baseline had a hazard ratio of 1.26 (95% CI 1.11 to 1.42, P = 0.00031). The authors stated that larger growth rate values were associated with a larger hazard. Non-epithelioid compared with epithelioid histology had a hazard ratio of 2.26 (95% CI 1.34 to 3.83, P = 0.0023).
  • In the final multivariate model, disease volume specific growth rate (continuous) had a hazard ratio of 1.31 (95% CI 1.13 to 1.53, P = 0.00045). Non-epithelioid histology had a hazard ratio of 2.28 (95% CI 1.33 to 3.93), and dyspnea (shortness of breath) had 3.20 (95% CI 1.53 to 6.71). ECOG performance status 1 or 2, compared with 0, had a hazard ratio of 1.69 (95% CI 1.05 to 2.72).
  • Model performance was measured with Heagerty's C-tau, on which 0.5 means no prognostic ability and 1.0 means perfect. It was 0.690 on the full cohort and 0.661 in leave-one-out cross-validation. Over 1000 random subsamples the mean was 0.660 (95% CI 0.556 to 0.746). A model based on disease volume alone gave 0.610.
• The authors' conclusion: The authors concluded that continuous pleural disease volumes from semiautomated segmentation were prognostically significant. They found increasing disease volume associated with poorer prognosis, alongside histology, dyspnea and ECOG performance status.
• Limitations the authors state:
  • The measured pleural disease is not necessarily the same as mesothelioma tumor. Where pleural effusion is mixed with tumor, separating the two can be difficult. Effusion was excluded only when it was localized and separate from tumor.
  • Whether pleural disease segmentation is feasible in routine clinical work is unclear.
  • The model has not been validated in a larger independent patient cohort.
  • Patients received cytotoxic chemotherapy, so the model may not apply directly to patients receiving biologically different treatments.
  • Dyspnea and performance status were fixed at their baseline values, because later data are not typically available.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer, Mesothelioma.

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, CT Scan, Chemotherapy, Histology, Performance Status, Dyspnea, Mesothelioma, Prognosis.

Related studies

• Lung volume measurements as a surrogate marker for patient response in malignant pleural mesothelioma — Z.E. Labby, S.G. Armato, et al. (2013)
• Soluble mesothelin, megakaryocyte potentiating factor, and osteopontin as markers of patient response and outcome in mesothelioma — K. Hollevoet, K. Nackaerts, et al. (2011)
• Thymidylate synthase and folyl-polyglutamate synthase are not clinically useful markers of response to pemetrexed in patients with malignant pleural mesothelioma — D.E. Lustgarten, C. Deshpande, et al. (2013)
• Chemotherapy management of malignant pleural mesothelioma: a phase II study comparing two popular chemotherapy regimens — E.E. Habib, E.S. Fahmy (2013)
• PI3K/mTOR signaling in mesothelioma patients treated with induction chemotherapy followed by extrapleural pneumonectomy — B.K. Bitanihirwe, M. Meerang, et al. (2014)
• Malignant peritoneal mesothelioma in a child: chemotherapy with gemcitabine and platinum was effective for the disease unresponsive to other treatments — S. Kobayashi, T. Waragai, et al. (2014)

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