Quantitative 3T multiparametric MRI of benign and malignant prostatic tissue in patients with and without local recurrent prostate cancer after external-beam radiation therapy.

BACKGROUND: Post-radiotherapy locally recurrent prostate cancer (PCa) patients are candidates for focal salvage treatment. Multiparametric MRI (mp-MRI) is attractive for tumor localization. However, radiotherapy-induced tissue changes complicate image interpretation. To develop focal salvage strategies, accurate tumor localization and distinction from benign tissue is necessary. PURPOSE: To quantitatively characterize radio-recurrent tumor and benign radiation-induced changes using mp-MRI, and investigate which sequences optimize the distinction between tumor and benign surroundings. STUDY TYPE: Prospective case-control. SUBJECTS: Thirty-three patients with biochemical failure after external-beam radiotherapy (cases), 35 patients without post-radiotherapy recurrent disease (controls), and 13 patients with primary PCa (untreated). FIELD STRENGTH/SEQUENCES: 3T; quantitative mp-MRI: T2 -mapping, ADC, and Ktrans and kep maps. ASSESSMENT: Quantitative image-analysis of prostatic regions, within and between cases, controls, and untreated patients. STATISTICAL TESTS: Within-groups: nonparametric Friedman analysis of variance with post-hoc Wilcoxon signed-rank tests; between-groups: Mann-Whitney tests. All with Bonferroni corrections. Generalized linear mixed modeling to ascertain the contribution of each map and location to tumor likelihood. RESULTS: Benign imaging values were comparable between cases and controls (P = 0.15 for ADC in the central gland up to 0.91 for kep in the peripheral zone), both with similarly high peri-urethral Ktrans and kep values (min-1 ) (median [range]: Ktrans = 0.22 [0.14-0.43] and 0.22 [0.14-0.36], P = 0.60, kep = 0.43 [0.24-0.57] and 0.48 [0.32-0.67], P = 0.05). After radiotherapy, benign central gland values were significantly decreased for all maps (P ≤ 0.001) as well as T2 , Ktrans , and kep of benign peripheral zone (all with P ≤ 0.002). All imaging maps distinguished recurrent tumor from benign peripheral zone, but only ADC, Ktrans , and kep were able to distinguish it from benign central gland. Recurrent tumor and peri-urethral Ktrans values were not significantly different (P = 0.81), but kep values were (P < 0.001). Combining all quantitative maps and voxel location resulted in an optimal distinction between tumor and benign voxels. DATA CONCLUSION: Mp-MRI can distinguish recurrent tumor from benign tissue. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:269-278.

Quantitative 3-T multi-parametric MRI and step-section pathology of recurrent prostate cancer patients after radiation therapy.

OBJECTIVES: Diagnosis of radio-recurrent prostate cancer using multi-parametric MRI (mp-MRI) can be challenging due to the presence of radiation effects. We aim to characterize imaging of prostate tissue after radiation therapy (RT), using histopathology as ground truth, and to investigate the visibility of tumor lesions on mp-MRI. METHODS: Tumor delineated histopathology slides from salvage radical prostatectomy patients, primarily treated with RT, were registered to MRI. Median T2-weighted, ADC, Ktrans, and kep values in tumor and other regions were calculated. Two radiologists independently performed mp-MRI-based tumor delineations which were compared with the true pathological extent. General linear mixed-effect modeling was used to establish the contribution of each imaging modality and combinations thereof in distinguishing tumor and benign voxels. RESULTS: Nineteen of the 21 included patients had tumor in the available histopathology slides. Recurrence was predominantly multifocal with large tumor foci seen after external beam radiotherapy, whereas these were small and sparse after low-dose-rate brachytherapy. MRI-based delineations missed small foci and slightly underestimated tumor extent. The combination of T2-weighted, ADC, Ktrans, and kep had the best performance in distinguishing tumor and benign voxels. CONCLUSIONS: Using high-resolution histopathology delineations, the real tumor extent and size were found to be underestimated on MRI. mp-MRI obtained the best performance in identifying tumor voxels. Appropriate margins around the visible tumor-suspected region should be included when designing focal salvage strategies. Recurrent tumor delineation guidelines are warranted. KEY POINTS: • Compared to the use of individual sequences, multi-parametric MRI obtained the best performance in distinguishing recurrent tumor from benign voxels. • Delineations based on mp-MRI miss smaller foci and slightly underestimate tumor volume of local recurrent prostate cancer. • Focal salvage strategies should include appropriate margins around the visible tumor.

Prediction of optimal needle configuration in the first fraction of cervix brachytherapy.

BACKGROUND AND PURPOSE: Applying needles in the first brachytherapy (BT) fraction for patients with locally advanced cervical cancer allows for more dose conformality and OAR sparing, but is more challenging than in subsequent fractions, as pre-implant imaging with applicator in situ is lacking. We investigate whether a needle simulation, a fixed needle configuration or a multidisciplinary discussion-based configuration can predict more accurately which applicator needle positions are best suited for use in the first BT fraction. MATERIALS AND METHODS: For 20 patients we retrospectively determined the "reference" needle configuration (RC) for the first BT fraction using magnetic resonance imaging (MRI) scans with applicator in situ. We simulated a pre-MRI needle configuration (PC) using the MRI made in the fourth week of external beam radiotherapy (EBRT) without applicator in situ. We generated a fixed needle configuration (FC) from the most common RC needles. Using Dice's similarity coefficient (DSC) we compared each of these needle configurations, including the clinically applied "multidisciplinary consensus" needle configuration (MC), with RC. We considered two scenarios: allowing up to ten needles (scenario 1), and limiting the needle number (scenario 2). The analysis was repeated omitting two mid-ventral needles previously determined as non-essential to treatment planning. RESULTS: For both scenarios, the median DSC for PC and FC was higher than for MC (scenario1:DSCPC = 0,78; DSCFC = 0,75; DSCMC = 0,57; scenario 2:DSCPC = 0,74; DSCFC = 0,73; DSCMC = 0,59), while omitting mid-ventral needles resulted in no statistically significant differences in DSC. CONCLUSIONS: The PC or FC method are at least as accurate as the MC, with the FC preferred for efficiency.

Multiparametric MRI Tumor Probability Model for the Detection of Locally Recurrent Prostate Cancer After Radiation Therapy: Pathologic Validation and Comparison With Manual Tumor Delineations.

PURPOSE: Focal salvage treatments of recurrent prostate cancer (PCa) after radiation therapy require accurate delineation of the target volume. Magnetic resonance imaging (MRI) is used for this purpose; however, radiation therapy-induced changes complicate image interpretation, and guidelines are lacking on the assessment and delineation of recurrent PCa. A tumor probability (TP) model was trained and independently tested using multiparametric magnetic resonance imaging (mp-MRI) of patients with radio-recurrent PCa. The resulting probability maps were used to derive target regions for radiation therapy treatment planning. METHODS AND MATERIALS: Two cohorts of patients with radio-recurrent PCa were used in this study. All patients underwent mp-MRI (T2 weighted, diffusion-weighted imaging, and dynamic contrast enhanced). A logistic regression model was trained using imaging features from 21 patients with biopsy-proven recurrence who qualified for salvage treatment. The test cohort consisted of 17 patients treated with salvage prostatectomy. The model was tested against histopathology-derived tumor delineations. The voxel-wise TP maps were clustered using k-means to generate a gross tumor volume (GTV) contour for voxel-level comparisons with manual tumor delineations performed by 2 radiologists and with histopathology-validated contours. Later, k-means was used with 3 clusters to define a clinical target volume (CTV), high-risk CTV, and GTV, with increasing tumor risk. RESULTS: In the test cohort, the model obtained a median (range) area under the curve of 0.77 (0.41-0.99) for the whole prostate. The GTV delineation resulted in a median sensitivity of 0.31 (0-0.87) and specificity of 0.97 (0.84-1.0) with no significant differences between model and manual delineations. The 3-level clustering GTV and high-risk CTV delineations had median sensitivities of 0.17 (0-0.59) and 0.49 (0-0.97) and specificities of 0.98 (0.84-1.00) and 0.94 (0.84-0.99), respectively. CONCLUSIONS: The TP model had a good performance in predicting voxel-wise presence of recurrent tumor. Model-derived tumor risk levels achieved sensitivity and specificity similar to manual delineations in localizing recurrent tumor. Voxel-wise TP derived from mp-MRI can in this way be incorporated for target definition in focal salvage of radio-recurrent PCa.

Needle use and dosimetric evaluation in cervical cancer brachytherapy using the Utrecht applicator.

BACKGROUND AND PURPOSE: To analyse the clinical use of needles and examine the feasibility to meet the planning criteria in three fractions of cervical cancer brachytherapy. Furthermore, to investigate whether the needles with the largest discrepancy between application and loading are essential to treatment planning. MATERIALS AND METHODS: For 22 patients we analysed the applied and loaded needle patterns, and examined the dosimetric results for small (<30 cm3) and large (≥30 cm3) CTVHR. We removed from the clinical plans (CP) the needles applied most, but with the lowest loading frequency and intensity and re-optimized these plans (RP). RESULTS: On average 5.8 needles were applied and 4.8 loaded per fraction, with average intensity 22% (17% for small, 29% for large CTVHR). Mid-lateral needles were applied and loaded most frequently and intensely. The average CTVHR D90% prescribed dose was 88.8 Gy (SD 4.2) EQD210, the average OAR [Formula: see text] limit was respected. Omitting the mid-ventral needles, minimal statistically significant differences were found in dose distributions between RP and CP. CONCLUSIONS: Applying on average 5.8 needles per fraction it was possible to meet the planning criteria for targets and OARs in three BT fractions for both small and large CTVHR. The mid-ventral needles were not essential in treatment planning, unless situated in the vicinity of the GTVres.

Biochemical recurrence prediction after radiotherapy for prostate cancer with T2w magnetic resonance imaging radiomic features.

BACKGROUND AND PURPOSE: High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15-35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT. MATERIALS AND METHODS: In a cohort of 120 high-risk patients, imaging features were extracted from the whole-prostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient's clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC). RESULTS: A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone. CONCLUSIONS: These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort.