Results from the ROC curve analysis using ΔADC, ΔVOL, and FLDA.

<p>Note: ΔADC: tumor ADC changes between measurements, ΔVOL: tumor volume changes between measurements, FLDA: result from Fisher's linear discriminant analysis, AUC: area under the curve.</p><p>*AUC using ΔADC vs. AUC using FLDA: p = 0.035.</p><p>Results from the ROC curve analysis using ΔADC, ΔVOL, and FLDA.</p

MRI acquisition parameters.

<p>MRI acquisition parameters.</p

Median tumor volumes ± standard deviation for each measurement and group.

<p>Note: VOL_B: baseline tumor volume; VOL_F: follow-up tumor volume, ΔVOL: tumor volume changes between measurements.</p><p>*Therapy VOL_B vs. therapy VOL_F: p = 0.034.</p>§<p>Control VOL_B vs. control VOL_F: p<0.001.</p>†<p>Therapy ΔVOL vs. control ΔVOL: p<0.001.</p><p>Median tumor volumes ± standard deviation for each measurement and group.</p

Box plots (first, second, and third quartile, range and outlier) of (a) ΔADC, (b) ΔVOL and (c) the results from the linear combination calculated with Fisher's linear discriminant analysis (FLDA) for each group and the corresponding p-value for the difference between them.

<p>Although significantly different, ΔADC and ΔVOL display distinctive overlaps between the two groups. The result from FLDA demonstrates a marked improvement in the group discrimination with nearly no overlap, resulting in a highly significant difference.</p

Region of interest (ROI) placement on <i>T</i>₂-weighted images over 4 example slices to measure the tumor volume.

<p>Total ROI extends over 22 slices for this measurement.</p

Median tumor ADCs ± standard deviation for each measurement and group.

<p>Note: ADC_B: baseline tumor ADC; ADC_F: follow-up tumor ADC, ΔADC: tumor ADC changes between measurements.</p><p>*Therapy ADC_B vs. therapy ADC_F: p<0.001.</p>§<p>Therapy ADC_F vs. control ADC_F: p<0.001.</p>†<p>Therapy ΔADC vs. control ΔADC: p = 0.027</p><p>Median tumor ADCs ± standard deviation for each measurement and group.</p

Fisher's linear discriminant anaylsis of volume and ADC data.

<p>Panel (a) illustrates thescatterplot of ΔVOL vs. ΔADC for each tumor. The solid grey line represents the optimal threshold determined by the ROC curve analysis; the linear regressions for each group (dashed line for therapy, dotted line for control) are annotated with Pearson's correlation coefficient r and p-value. (b) Results from the FLDA-derived linear combination of ΔADC and ΔVOL (FLDA = 0.0033×ΔVOL[%] - 1.0366×ΔADC[10⁻³ mm²/s]).</p

Region of interest (ROI) placement on diffusion-weighted images with b = 10 s/mm² over 4 example slices to calculate the median tumor ADC.

<p>Total ROI extends over 10 slices for this measurement.</p

ROC curve analysis using (a) ΔADC, (b) ΔVOL and (c) the result from the linear combination calculated with Fisher's linear discriminant analysis (FLDA) to illustrate performance in differentiating therapy from control group.

<p>The combined approach using FLDA outperforms the use of ΔADC and ΔVOL notably. Optimal sensitivity and specificity for each parameter and the corresponding thresholds are summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106970#pone-0106970-t003" target="_blank">Table 3</a>. * AUC using ΔADC vs AUC using FLDA: p = 0.035.</p

Voxelwise calculated ADC maps in the subcutaneous colon carcinoma of a therapy animal before and after 6 days of therapy with regorafenib laid over diffusion-weighted images with b = 10 s/mm².

<p>The ADC maps display a prominent increase, which is also reflected in the median tumor ADC value for this animal: ADC_B = 0.762×10⁻³mm²/s at day 0, ADC_F = 1.137×10⁻³mm²/s at day 7.</p