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

• Published in: PloS one 2017-06, Vol.12 (6), p.e0178901-e0178901
• Main Authors: Rouvière, Olivier, Dagonneau, Tristan, Cros, Fanny, Bratan, Flavie, Roche, Laurent, Mège-Lechevallier, Florence, Ruffion, Alain, Crouzet, Sébastien, Colombel, Marc, Rabilloud, Muriel
• Format: Article
• Language: English
• Subjects: Accuracy; Aged; Antigens; Area Under Curve; Bioengineering; Biology and Life Sciences; Cancer; Cancer surgery; Care and treatment; Clinical significance; Density; Diagnosis; Diagnostic systems; Diagnostic tests; Diffusion; Diffusion Magnetic Resonance Imaging; Human health and pathology; Humans; Image Enhancement; Imaging; Life Sciences; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Malignancy; Mathematical models; Medical diagnosis; Medicine and Health Sciences; Middle Aged; NMR; Nuclear magnetic resonance; Optimization; Patients; Prostate cancer; Prostatectomy; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Prostatic Neoplasms - surgery; Readers; Research and Analysis Methods; Resonance; ROC Curve; Sequences; Urological surgery; Urology; Urology and Nephrology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0178901

To assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa). 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). 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. 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. 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. 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. Combination of simple variables can match the Likert score's results. The optimal combination depends on the definition of csPCa.