Management and outcomes of Gleason six prostate cancer detected on needle biopsy: A single-surgeon experience over 6 years

Heterotopic Splenic Tissue Mimicking Metastases on Magnetic Resonance Imaging

Background

Heterotopic splenic tissue can occur following splenectomy and is typically asymptomatic, often discovered incidentally during imaging for other conditions. This benign condition may mimic malignant processes, posing diagnostic challenges especially in patients with a history of cancer or concurrent malignancy.

Case description

We report the case of a 60-year-old male with a history of well-controlled hypertension and a splenectomy following a traumatic injury at age 7. The patient underwent routine screening which revealed elevated prostate-specific antigen (PSA) levels. Subsequent magnetic resonance imaging (MRI) identified suspicious lesions in the prostate and a left lower quadrant mass. Prostate biopsy confirmed an adenocarcinoma with a Gleason score of 6, while biopsy of the abdominal mass revealed heterotopic splenic tissue. The management strategy included active surveillance for prostate cancer, considering the tumour's low aggressiveness and the benign nature of the splenic tissue.

Conclusions

This case highlights the importance of considering heterotopic splenic tissue in differential diagnosis when evaluating abdominal masses in patients with a history of splenectomy. Accurate diagnosis through careful imaging and biopsy is crucial to avoid misdiagnosis and unnecessary treatments, particularly in patients with concurrent malignancies.

LEARNING POINTS

In patients who have had a prior splenectomy for trauma, heterotopic splenic tissue may be mistaken for metastases on MRI.

Automatic segmentation of prostate MRI based on 3D pyramid pooling Unet

PURPOSE

Automatic segmentation of prostate magnetic resonance (MR) images is crucial for the diagnosis, evaluation, and prognosis of prostate diseases (including prostate cancer). In recent years, the mainstream segmentation method for the prostate has been converted to convolutional neural networks. However, owing to the complexity of the tissue structure in MR images and the limitations of existing methods in spatial context modeling, the segmentation performance should be improved further.

METHODS

In this study, we proposed a novel 3D pyramid pool Unet that benefits from the pyramid pooling structure embedded in the skip connection (SC) and the deep supervision (DS) in the up-sampling of the 3D Unet. The parallel SC of the conventional 3D Unet network causes low-resolution information to be sent to the feature map repeatedly, resulting in blurred image features. To overcome the shortcomings of the conventional 3D Unet, we merge each decoder layer with the feature map of the same scale as the encoder and the smaller scale feature map of the pyramid pooling encoder. This SC combines the low-level details and high-level semantics at two different levels of feature maps. In addition, pyramid pooling performs multifaceted feature extraction on each image behind the convolutional layer, and DS learns hierarchical representations from comprehensive aggregated feature maps, which can improve the accuracy of the task.

RESULTS

Experiments on 3D prostate MR images of 78 patients demonstrated that our results were highly correlated with expert manual segmentation. The average relative volume difference and Dice similarity coefficient of the prostate volume area were 2.32% and 91.03%, respectively.

CONCLUSION

Quantitative experiments demonstrate that, compared with other methods, the results of our method are highly consistent with the expert manual segmentation.

Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets

PURPOSE

Accurate regional segmentation of the prostate boundaries on magnetic resonance (MR) images is a fundamental requirement before automated prostate cancer diagnosis can be achieved. In this paper, we describe a novel methodology to segment prostate whole gland (WG), central gland (CG), and peripheral zone (PZ), where PZ + CG = WG, from T2W and apparent diffusion coefficient (ADC) map prostate MR images.

METHODS

We designed two similar models each made up of two U-Nets to delineate the WG, CG, and PZ from T2W and ADC map MR images, separately. The U-Net, which is a modified version of a fully convolutional neural network, includes contracting and expanding paths with convolutional, pooling, and upsampling layers. Pooling and upsampling layers help to capture and localize image features with a high spatial consistency. We used a dataset consisting of 225 patients (combining 153 and 72 patients with and without clinically significant prostate cancer) imaged with multiparametric MRI at 3 Tesla.

RESULTS AND CONCLUSION

Our proposed model for prostate zonal segmentation from T2W was trained and tested using 1154 and 1587 slices of 100 and 125 patients, respectively. Median of Dice similarity coefficient (DSC) on test dataset for prostate WG, CG, and PZ were 95.33 ± 7.77%, 93.75 ± 8.91%, and 86.78 ± 3.72%, respectively. Designed model for regional prostate delineation from ADC map images was trained and validated using 812 and 917 slices from 100 and 125 patients. This model yielded a median DSC of 92.09 ± 8.89%, 89.89 ± 10.69%, and 86.1 ± 9.56% for prostate WG, CG, and PZ on test samples, respectively. Further investigation indicated that the proposed algorithm reported high DSC for prostate WG segmentation from both T2W and ADC map MR images irrespective of WG size. In addition, segmentation accuracy in terms of DSC does not significantly vary among patients with or without significant tumors.

SIGNIFICANCE

We describe a method for automated prostate zonal segmentation using T2W and ADC map MR images independent of prostate size and the presence or absence of tumor. Our results are important in terms of clinical perspective as fully automated methods for ADC map images, which are considered as one of the most important sequences for prostate cancer detection in the PZ and CG, have not been reported previously.