Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.
Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.
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磁共振成像中前列腺癌的体素水平分类：使用四分量限制频谱成像提高准确性。
DOI：
10.1002/jmri.27623
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Seibert TM
中科院分区：
文献类型：
--
作者：
Feng CH;Conlin CC;Batra K;Rodríguez-Soto AE;Karunamuni R;Simon A;Kuperman J;Rakow-Penner R;Hahn ME;Dale AM;Seibert TM
扩散MRI是检测前列腺癌（PCa）不可或缺的一部分，但传统的表观扩散系数（ADC）无法捕获前列腺组织的复杂性，并且往往会产生无法明显突出癌症的噪声图像。最近发现一个四室限制性频谱成像（RSI 4）模型，以最佳的特征盆腔扩散信号，和模型系数最慢的扩散室，RSI 4-C1，产生最大的肿瘤的显着性。评价四室光谱成像模型（RSI 4-C1）的最慢扩散室作为前列腺癌（PCa）的定量体素级分类器。回顾性46名因疑似前列腺癌接受扩展MRI采集方案的男性。23名男性患有良性前列腺，另外23名男性患有前列腺癌。3 T，多壳扩散加权和轴向T2加权序列。高置信度的癌症体素由专家共识，使用成像数据和活检结果划定。没有检测到癌症的患者的整个前列腺被认为是良性的。采用弥散图像计算RSI 4-C1和常规ADC值。还生成了分类器图像。前列腺癌从良性前列腺组织的体素水平的歧视进行了评估，通过受试者工作特征（ROC）曲线与患者水平的情况下，自举生成。将RSI 4-C1与常规ADC的两个指标进行比较：ROC曲线下面积（AUC）和90%灵敏度的假阳性率（FPR 90）。使用bootstrap差异评估统计学显著性，双侧α = 0.05。RSI 4-C1优于常规ADC，AUC更大[平均值0.977（95% CI 0.951-0.991）vs. 0.922（0.878-0.948）]，FPR 90更低[0.032（0.009-0.082）vs. 0.201（0.132-0.290）]。这些改善具有统计学意义（p<0.05）。RSI 4-C1产生了一个定量的，体素级分类的PCa是上级传统的ADC。具有低假阳性率的RSI分类器图像可能会改善PCa检测，并促进靶向活检和治疗计划等临床应用。
Diffusion MRI is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4-C1, yielded greatest tumor conspicuity. To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4-C1) as a quantitative voxel-level classifier of prostate cancer (PCa). Retrospective Forty-six men who underwent an extended MRI acquisition protocol for suspected prostate cancer. Twenty-three men had benign prostates, and the other 23 men had prostate cancer. 3T, multi-shell diffusion-weighted and axial T2-weighted sequences. High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4-C1 and conventional ADC. Classifier images were also generated. Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4-C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05. RSI4-C1 outperformed conventional ADC, with greater AUC [mean 0.977 (95% CI 0.951–0.991) vs. 0.922 (0.878–0.948)] and lower FPR90 [0.032 (0.009–0.082) vs. 0.201 (0.132–0.290)]. These improvements were statistically significant (p<0.05). RSI4-C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.
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