Abstract
Radiotherapy is the choice of treatment for locally advanced stages of the cervical cancers, one of the leading female cancers. Because of intrinsic factors, tumors of same clinical stage and histological type often exhibit differential radioresponse. Radiotherapy regimen, from first fraction of treatment to clinical evaluation of response, spans more than 4 months. Clinical assessment by degree of tumor shrinkage is the only routinely practiced method to evaluate the tumor response. Hence, a need is created for development new methodologies that can predict the tumor response to radiotherapy at an early stage of the treatment which can lead to tailor-made protocols. To explore the feasibility of prediction of tumor radioresponse, Raman spectra of cervix cancer tissues that were collected before (malignant) and 24 h after patient was treated with 2nd fraction of radiotherapy (RT) were recorded. Data were analyzed by Principal Components Analysis (PCA) and results were correlated with clinical evaluation of radioresponse. Mean Raman spectra of RT tissues corresponding to different levels of tumor response, complete, partial, and no response, showed minute but significant variations. The unsupervised PCA of malignant tissues failed to provide any classification whereas RT spectra gave clear classification between responding (complete and partial response) and nonresponding conditions as well as a tendency of separation among responding conditions. These results were corroborated by supervised classification, by means of discrimination parameters: Mahalanobis distance and spectral residuals. Thus, findings of the study suggest the feasibility of Raman spectroscopic prediction of tumor radioresponse in cervical cancers.
| Original language | English |
|---|---|
| Pages (from-to) | 530-537 |
| Number of pages | 8 |
| Journal | Biopolymers |
| Volume | 89 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 01-06-2008 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Biophysics
- Biochemistry
- Biomaterials
- Organic Chemistry
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