Feasibility study on the use of CMOS sensors as detectors in radioguided surgery with β-- emitters

First-in-human validation of a DROP-IN β-probe for robotic radioguided surgery: defining optimal signal-to-background discrimination algorithm

PURPOSE

In radioguided surgery (RGS), radiopharmaceuticals are used to generate preoperative roadmaps (e.g., PET/CT) and to facilitate intraoperative tracing of tracer avid lesions. Within RGS, there is a push toward the use of receptor-targeted radiopharmaceuticals, a trend that also has to align with the surgical move toward minimal invasive robotic surgery. Building on our initial ex vivo evaluation, this study investigates the clinical translation of a DROP-IN β probe in robotic PSMA-guided prostate cancer surgery.

METHODS

A clinical-grade DROP-IN β probe was developed to support the detection of PET radioisotopes (e.g., 68 Ga). The prototype was evaluated in 7 primary prostate cancer patients, having at least 1 lymph node metastases visible on PSMA-PET. Patients were scheduled for radical prostatectomy combined with extended pelvic lymph node dissection. At the beginning of surgery, patients were injected with 1.1 MBq/kg of [68Ga]Ga-PSMA. The β probe was used to trace PSMA-expressing lymph nodes in vivo. To support intraoperative decision-making, a statistical software algorithm was defined and optimized on this dataset to help the surgeon discriminate between probe signals coming from tumors and healthy tissue.

RESULTS

The DROP-IN β probe helped provide the surgeon with autonomous and highly maneuverable tracer detection. A total of 66 samples (i.e., lymph node specimens) were analyzed in vivo, of which 31 (47%) were found to be malignant. After optimization of the signal cutoff algorithm, we found a probe detection rate of 78% of the PSMA-PET-positive samples, a sensitivity of 76%, and a specificity of 93%, as compared to pathologic evaluation.

CONCLUSION

This study shows the first-in-human use of a DROP-IN β probe, supporting the integration of β radio guidance and robotic surgery. The achieved competitive sensitivity and specificity help open the world of robotic RGS to a whole new range of radiopharmaceuticals.

Deep Learning Signal Discrimination for Improved Sensitivity at High Specificity for CMOS Intraoperative Probes

The challenge in delineating the boundary between cancerous and healthy tissue during cancer resection surgeries can be addressed with the use of intraoperative probes to detect cancer cells labeled with radiotracers to facilitate excision. In this study, deep learning algorithms for background gamma ray signal rejection were explored for an intraoperative probe utilizing CMOS monolithic active pixel sensors optimized toward the detection of internal conversion electrons from [Formula: see text]Tc. Two methods utilizing convolutional neural networks (CNNs) were explored for beta-gamma discrimination: 1) classification of event clusters isolated from the sensor array outputs (SAOs) from the probe and 2) semantic segmentation of event clusters within an acquisition frame of an SAO which provides spatial information on the classification. The feasibility of the methods in this study was explored with several radionuclides including 14C, 57Co, and [Formula: see text]Tc. Overall, the classification deep network is able to achieve an improved area under the curve (AUC) of the receiver operating characteristic (ROC), giving 0.93 for 14C beta and [Formula: see text]Tc gamma clusters, compared to 0.88 for a more conventional feature-based discriminator. Further optimization of the lower left region of the ROC by using a customized AUC loss function during training led to an improvement of 31% in sensitivity at low false positive rates compared to the conventional method. The segmentation deep network is able to achieve a mean dice score of 0.93. Through the direct comparison of all methods, the classification method was found to have a better performance in terms of the AUC.

Beta radioguided surgery: towards routine implementation?

INTRODUCTION

In locally or locally advanced solid tumors, surgery still remains a fundamental treatment method. However, conservative resection is associated with high collateral damage and functional limitations of the patient. Furthermore, the presence of residual tumor tissue following conservative surgical treatment is currently a common cause of locally recurrent cancer or of distant metastases. Reliable intraoperative detection of small cancerous tissue would allow surgeons to selectively resect malignant areas: this task can be achieved by means of image-guided surgery, such as beta radioguided surgery (RGS).

EVIDENCE ACQUISITION

In this paper, a comprehensive review of beta RGS is given, starting from the physical principles that differentiate beta from gamma radiation, that has already its place in nuclear medicine current practice. Also, the recent clinical feasibility of using Cerenkov radiation is discussed.

EVIDENCE SYNTHESIS

Despite being first proposed several decades ago, only in the last years a remarkable interest in beta RGS has been observed, probably driven by the diffusion of PET radio tracers. Today several different approaches are being pursued to assess the effectiveness of such a technique, including both beta+ and beta- emitting radiopharmaceuticals.

CONCLUSIONS

Beta RGS shows some peculiarities that can present it as a very promising complementary technique to standard procedures. Good results are being obtained in several tests, both ex vivo and in vivo. This might however be the time to initiate the trials to demonstrate the real clinical value of these technologies with seemingly clear potential.