OBJECTIVES: We introduce biochemistry as a second dimension to Gleason grading, using Fourier transform infrared (FTIR) microspectroscopy. For the first time, we correlate FTIR spectra derived from prostate cancer (pCA) tissue with Gleason score and the clinical stage of the tumour at time of biopsy. METHODS: Serial sections from paraffin-embedded pCA tissue were collected. One was stained with hematoxylin and eosin and Gleason scored; FTIR spectra were collected from malignant locations using a second unstained section. FTIR spectra, representing different Gleason grades, were used to construct a diagnostic classifier for pCA using linear discriminant analysis (LDA). This model was blind tested using 383 IR spectra from 36 biopsies. RESULTS: Using a three-band Gleason criteria, we obtained sensitivity of > or =70% for the FTIR-LDA model to predict Gleason <7,=7, and >7, with specificities of > or =81%. Using a threshold of Gleason/FTIR-LDA score of > or =8, we obtained a sensitivity and specificity of 71% and 67%, respectively, for the correlation with metastatic tumours using the FTIR-LDA system and 85% and 63%, respectively, for the correlation of metastatic tumours using the Gleason system. CONCLUSIONS: There is a correlation between tissue architecture using Gleason score with tissue biochemistry using FTIR-LDA. Both systems are similar in their performance in predicting metastatic behaviour in tumours from individual patients.
OBJECTIVES: We introduce biochemistry as a second dimension to Gleason grading, using Fourier transform infrared (FTIR) microspectroscopy. For the first time, we correlate FTIR spectra derived from prostate cancer (pCA) tissue with Gleason score and the clinical stage of the tumour at time of biopsy. METHODS: Serial sections from paraffin-embedded pCA tissue were collected. One was stained with hematoxylin and eosin and Gleason scored; FTIR spectra were collected from malignant locations using a second unstained section. FTIR spectra, representing different Gleason grades, were used to construct a diagnostic classifier for pCA using linear discriminant analysis (LDA). This model was blind tested using 383 IR spectra from 36 biopsies. RESULTS: Using a three-band Gleason criteria, we obtained sensitivity of > or =70% for the FTIR-LDA model to predict Gleason <7,=7, and >7, with specificities of > or =81%. Using a threshold of Gleason/FTIR-LDA score of > or =8, we obtained a sensitivity and specificity of 71% and 67%, respectively, for the correlation with metastatic tumours using the FTIR-LDA system and 85% and 63%, respectively, for the correlation of metastatic tumours using the Gleason system. CONCLUSIONS: There is a correlation between tissue architecture using Gleason score with tissue biochemistry using FTIR-LDA. Both systems are similar in their performance in predicting metastatic behaviour in tumours from individual patients.
Authors: Abegail Santillan; Rock Christian Tomas; Ruth Bangaoil; Rolando Lopez; Maria Honolina Gomez; Allan Fellizar; Antonio Lim; Lorenzo Abanilla; Maria Cristina Ramos; Leonardo Guevarra; Pia Marie Albano Journal: Anal Bioanal Chem Date: 2021-02-10 Impact factor: 4.142
Authors: L Suzanne Leslie; Tomasz P Wrobel; David Mayerich; Snehal Bindra; Rajyasree Emmadi; Rohit Bhargava Journal: PLoS One Date: 2015-06-03 Impact factor: 3.240
Authors: James R Hands; Graeme Clemens; Ryan Stables; Katherine Ashton; Andrew Brodbelt; Charles Davis; Timothy P Dawson; Michael D Jenkinson; Robert W Lea; Carol Walker; Matthew J Baker Journal: J Neurooncol Date: 2016-02-13 Impact factor: 4.130
Authors: Matthew J Baker; Júlio Trevisan; Paul Bassan; Rohit Bhargava; Holly J Butler; Konrad M Dorling; Peter R Fielden; Simon W Fogarty; Nigel J Fullwood; Kelly A Heys; Caryn Hughes; Peter Lasch; Pierre L Martin-Hirsch; Blessing Obinaju; Ganesh D Sockalingum; Josep Sulé-Suso; Rebecca J Strong; Michael J Walsh; Bayden R Wood; Peter Gardner; Francis L Martin Journal: Nat Protoc Date: 2014-07-03 Impact factor: 13.491