RESUMO
OBJECTIVES: To evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) and their machine-learning-based texture analysis for the detection and assessment of severity in prostate cancer (PCa). MATERIALS AND METHODS: Eighty-eight patients underwent MRI on a 3 T scanner after giving informed consent. IVIM-DKI data were acquired using 13 b values (0-2000 s/mm2) and analyzed using the IVIM-DKI model with the total variation (TV) method. PCa patients were categorized into two groups: clinically insignificant prostate cancer (CISPCa) (Gleason grade ≤ 6) and clinically significant prostate cancer (CSPCa) (Gleason grade ≥ 7). One-way analysis-of-variance, t test, and receiver operating characteristic analysis was performed to measure the discriminative ability to detect PCa using IVIM-DKI parameters. A chi-square test was used to select important texture features of apparent diffusion coefficient (ADC) and IVIM-DKI parameters. These selected texture features were used in an artificial neural network for PCa detection. RESULTS: ADC and diffusion coefficient (D) were significantly lower (p < 0.001), and kurtosis (k) was significantly higher (p < 0.001), in PCa as compared with benign prostatic hyperplasia (BPH) and normal peripheral zone (PZ). ADC, D, and k showed high areas under the curves (AUCs) of 0.92, 0.89, and 0.88, respectively, in PCa detection. ADC and D were significantly lower (p < 0.05) as compared with CISPCa versus CSPCa. D for detecting CSPCa was high, with an AUC of 0.63. A negative correlation of ADC and D with GS (ADC, ρ = -0.33; D, ρ = -0.35, p < 0.05) and a positive correlation of k with GS (ρ = 0.22, p < 0.05) were observed. Combined IVIM-DKI texture showed high AUC of 0.83 for classification of PCa, BPH, and normal PZ. CONCLUSION: D, f, and k computed using the IVIM-DKI model with the TV method were able to differentiate PCa from BPH and normal PZ. Texture features of combined IVIM-DKI parameters showed high accuracy and AUC in PCa detection.
RESUMO
BACKGROUND: Frailty is a major challenge for healthcare systems in ageing societies. This dynamic state of health is a reflection of reduced reserve in various organ systems and enhanced vulnerability to stressors. Research in this area of geriatrics and gerontology is limited in low- and middle-income countries (LMICs) like India. This study is directed at development of a culturally appropriate and validated assessment scale for frailty among older Indians. METHODS: After extensive review of the literature on existing scales, a preliminary draft scale was formed. This draft was pre- and pilot-tested to check feasibility and modified accordingly. The final scale was validated on 107 older adults by confirmatory factor analysis and was named the Frailty Assessment and Screening Tool (FAST). The Fried's frailty phenotype was also administered on the same 107 older adults and scores of both were co-related. Suitable cut-off scores were found for frail and pre-frail older adults. RESULTS: The final version of the FAST consisted of 14 questions pertaining to 10 domains. It has good reliability. Cronbach's alpha co-efficient was 0.99; test-retest reliability was 0.97 and validity by confirmatory factor analysis was adequate. The Kaiser-CMeyer-Olkin (KMO) of sampling adequacy was 0.699, and Bartlett's test of sphericity was significant (χ 2 = 353.471, p < 0.001). FAST scores had a cut-off of ≥ 7/14 for frail and ≥ 5/14 for pre-frail elderly. CONCLUSION: The FAST is a validated tool with good psychometric properties. It is expected that it will be helpful in screening pre-frail and frail older adults in India and other LMICs and guide in clinical decision making for intervention.