Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers

<div><p>Purpose</p><p>To assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa).</p><p>Materials and methods</p><p>We used a prospective database of 262 patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) imaging before prostatectomy. For each lesion, two independent readers (R1, R2) prospectively defined nine features: shape, volume (V_Max), signal abnormality on each pulse sequence, number of pulse sequences with a marked (S_Max) and non-visible (S_Min) abnormality, likelihood of extracapsular extension (ECE) and PSA density (dPSA). Overall likelihood of malignancy was assessed using a 5-level Likert score. Features were evaluated using the area under the receiver operating characteristic curve (AUC). csPCa was defined as Gleason ≥7 cancer (csPCa-A), Gleason ≥7(4+3) cancer (csPCa-B) or Gleason ≥7 cancer with histological extraprostatic extension (csPCa-C),</p><p>Results</p><p>For csPCa-A, the Signal1 model (S_Max+S_Min) provided the best combination of signal-related variables, for both readers. The performance was improved by adding V_Max, ECE and/or dPSA, but not shape. All models performed better with DCE findings than without.</p><p>When moving from csPCa-A to csPCa-B and csPCa-C definitions, the added value of V_Max, dPSA and ECE increased as compared to signal-related variables, and the added value of DCE decreased.</p><p>For R1, the best models were Signal1+ECE+dPSA (AUC = 0,805 [95%CI:0,757–0,866]), Signal1+V_Max+dPSA (AUC = 0.823 [95%CI:0.760–0.893]) and Signal1+ECE+dPSA [AUC = 0.840 (95%CI:0.774–0.907)] for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0.844 [95%CI:0.806–0.877, p = 0.11], 0.841 [95%CI:0.799–0.876, p = 0.52]) and 0.849 [95%CI:0.811–0.884, p = 0.49], respectively.</p><p>For R2, the best models were Signal1+V_Max+dPSA (AUC = 0,790 [95%CI:0,731–0,857]), Signal1+V_Max (AUC = 0.813 [95%CI:0.746–0.882]) and Signal1+ECE+V_Max (AUC = 0.843 [95%CI: 0.781–0.907]) for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0. 829 [95%CI:0.791–0.868, p = 0.13], 0.790 [95%CI:0.742–0.841, p = 0.12]) and 0.808 [95%CI:0.764–0.845, p = 0.006]), respectively.</p><p>Conclusion</p><p>Combination of simple variables can match the Likert score’s results. The optimal combination depends on the definition of csPCa.</p></div

Axial multiparametric MR images acquired on scanner B at 3T, in a 58 year-old patient with a PSA density of 0.28 ng/mL/mL.

<p>A) T2-weighted image. B) Apparent diffusion coefficient map. C) Dynamic contrast-enhanced image. One suspicious lesion was described by both readers in the right peripheral zone (A-C, arrow). The lesion was noted as nodular without mass effect by both readers. S_T2, S_DW and S_DCE were respectively marked, marked and moderate for both readers. V_Max was 2.0 cc and 2.1 cc for readers 1 and 2 respectively. The ECE and Likert scores were respectively 2/5 and 5/5 for both readers. Analysis of the prostatectomy specimen showed a matching Gleason 9 (4+5) cancer with a histological volume of 1.6 cc.</p

Assessed individual variables.

<p>Assessed individual variables.</p

Performances of multivariable models in characterizing csPCa-B in PZ.

<p>Performances of multivariable models in characterizing csPCa-B in PZ.</p

Performances of multivariable models in characterizing csPCa-A in PZ.

<p>Performances of multivariable models in characterizing csPCa-A in PZ.</p

Performances of multivariable models in characterizing csPCa-C in PZ.

<p>Performances of multivariable models in characterizing csPCa-C in PZ.</p

Performance of individual variables in characterizing csPCa-A in PZ.

<p>Performance of individual variables in characterizing csPCa-A in PZ.</p

Evolution of diagnostic performances of models and Likert scores in PZ as a function of csPCa definition.

<p>Evolution of diagnostic performances of models and Likert scores in PZ as a function of csPCa definition.</p