Quantitative diagnosis of malignant pleural effusions by single-cell mechanophenotyping

Henry T.K. Tse, Daniel R. Gossett, Yo Sup Moon, Mahdokht Masaeli, Marie Sohsman, Yong Ying, Kimberly Mislick, Ryan P. Adams, Jianyu Rao, Dino Di Carlo

Research output: Contribution to journalArticlepeer-review

213 Scopus citations


Biophysical characteristics of cells are attractive as potential diagnostic markers for cancer. Transformation of cell state or phenotype and the accompanying epigenetic, nuclear, and cytoplasmic modifications lead to measureable changes in cellular architecture. We recently introduced a technique called deformability cytometry (DC) that enables rapid mechanophenotyping of single cells in suspension at rates of 1000 cells/s - a throughput that is comparable to traditional flow cytometry. We applied this technique to diagnose malignant pleural effusions, in which disseminated tumor cells can be difficult to accurately identify by traditional cytology. An algorithmic diagnostic scoring system was developed on the basis of quantitative features of two-dimensional distributions of single-cell mechanophenotypes from 119 samples. The DC scoring system classified 63% of the samples into two high-confidence regimes with 100% positive predictive value or 100% negative predictive value, and achieved an area under the curve of 0.86. This performance is suitable for a prescreening role to focus cytopathologist analysis time on a smaller fraction of difficult samples. Diagnosis of samples that present a challenge to cytology was also improved. Samples labeled as "atypical cells," which require additional time and follow-up, were classified in high-confidence regimes in 8 of 15 cases. Further, 10 of 17 cytology-negative samples corresponding to patients with concurrent cancer were correctly classified as malignant or negative, in agreement with 6-month outcomes. This study lays the groundwork for broader validation of label-free quantitative biophysical markers for clinical diagnoses of cancer and inflammation, which could help to reduce laboratory workload and improve clinical decision-making.

Original languageEnglish (US)
Article number212ra163
JournalScience Translational Medicine
Issue number212
StatePublished - Nov 20 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Medicine


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