PET guidance in prostate cancer radiotherapy: Quantitative imaging to predict response and guide treatment

Breathing-induced Errors in Quantification and Description of Dominant Intra-Prostatic Lesions (Dils) in PET Images: A Simulation Study by Means of The 4D NCAT Phantom

BACKGROUND

Respiratory movement and the motion range of the diaphragm can affect the quality and quantity of prostate images.

OBJECTIVE

This study aimed to investigate the magnitude of respiratory-induced errors to determine Dominant Intra- prostatic Lesions (DILs) in positron emission tomography (PET) images.

MATERIAL AND METHODS

In this simulation study, we employed the 4D NURBS-based cardiac-torso (4D-NCAT) phantom with a realistic breathing model to simulate the respiratory cycles of a patient to assess the displacement, volume, maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), signal to noise ratio (SNR), and the contrast of DILs in frames within the respiratory cycle.

RESULTS

Respiration in a diaphragm motion resulted in the maximum superior-inferior displacement of 3.9 and 6.1 mm, and the diaphragm motion amplitudes of 20 and 35 mm. In a no-motion image, the volume measurement of DILs had the smallest percentage of errors. Compared with the no-motion method, the percentages of errors in the average method in 20 and 35 mm- diaphragm motion were 25% and 105%, respectively. The motion effect was significantly reduced in terms of the values of SUV_max and SUV_mean in comparison with the values of SUV_max and SUV_mean in no- motion images. The contrast values in respiratory cycle frames were at a range of 3.3-19.2 mm and 6.5-46 for diaphragm movements' amplitudes of 20 and 35 mm.

CONCLUSION

The respiratory movement errors in quantification and delineation of DILs were highly dependent on the range of motion, while the average method was not suitable to precisely delineate DILs in PET/CT in the dose-painting technique.

Evaluation of Dose-Painting in the Dominant Intraprostatic Lesions by Radiobiological Parameters using 68Ga- PSMA PET/CT

BACKGROUND

Patients diagnosed with dominant intraprostatic lesions (DIL) may need radiation doses over than 80 Gy. Dose-painting by contours (DPC) is a useful technique which helps the patients. Dose-painting approach need to be evaluated.

OBJECTIVE

To evaluate the DCP technique in the case of boosting the DILs by radiobiological parameters, tumor control probability (TCP), and normal tissue complication probability (NTCP) via PET/CT images traced by ⁶⁸Ga-PSMA.

MATERIAL AND METHODS

In this analytical study, ⁶⁸Ga-PSMA PET/CT images were obtained from patients with DILs that were delineated using the Fuzzy c-mean (FCM) algorithm and thresholding methods. The protocol of therapy included two phases; at the first phase (ph1), a total dose of 72 Gy in 36 fractions were delivered to the planning target volume (PTV1); the seconds phase consisted of the application of variable doses to the PTV2. Moreover, two concepts were also considered to calculate the TCP using the Zaider-Minerbo model.

RESULTS

The lowest volume in DILs belonged to the DIL1 extracted by the FCM method. According to dose-volume parameters of the rectum and bladder, by the increase in the PTV dose higher than 92 Gy, the amounts of rectum and bladder doses are increased. There was no difference between the TCPs of DILs at doses higher than 86 Gy and 100 Gy for ordinary and high clone density, respectively.

CONCLUSION

Consequently, our dose-painting approach for DILs, extracted by the FCM method via PET/CT images, can reduce the total dose for prostate radiation with 100% tumor control and less normal tissue complications.

Deep phenotyping for precision medicine in Parkinson's disease

A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.

Technical Note: Adapting a GE SIGNA PET/MR scanner for radiotherapy

PURPOSE

Simultaneous collection of PET and MR data for radiotherapy purposes are useful for, for example, target definition and dose escalations. However, a prerequisite for using PET/MR in the radiotherapy workflow is the ability to image the patient in treatment position. The aim of this work was to adapt a GE SIGNA PET/MR scanner to image patients for radiotherapy treatment planning and evaluate the impact on signal-to-noise (SNR) of the MR images, and the accuracy of the PET attenuation correction.

METHOD

A flat tabletop and a coil holder were developed to image patients in the treatment position, avoid patient contour deformation, and facilitate attenuation correction of flex coils. Attenuation corrections for the developed hardware and an anterior array flex coil were also measured and implemented to the PET/MR system to minimize PET quantitation errors. The reduction of SNR in the MR images due to the added distance between the coils and the patient was evaluated using a large homogenous saline-doped water phantom, and the activity quantitation errors in PET imaging were evaluated with and without the developed attenuation corrections.

RESULT

We showed that the activity quantitation errors in PET imaging were within ±5% when correcting for attenuation of the flat tabletop, coil holder, and flex coil. The SNR of the MRI images were reduced to 74% using the tabletop, and 66% using the tabletop and coil holders.

CONCLUSION

We present a tabletop and coil holder for an anterior array coil to be used with a GE SIGNA PET/MR scanner, for scanning patients in the radiotherapy work flow. Implementing attenuation correction of the added hardware from the radiotherapy setup leads to acceptable PET image quantitation. The drop in SNR in MR images may require adjustment of the imaging protocols.

Radiomics in radiooncology - Challenging the medical physicist

PURPOSE

Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions.

METHODS

Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach.

RESULTS

Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team.

CONCLUSIONS

The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.

The role of medical physics in prostate cancer radiation therapy

Medical physics, both as a scientific discipline and clinical service, hugely contributed and still contributes to the advances in the radiotherapy of prostate cancer. The traditional translational role in developing and safely implementing new technology and methods for better optimizing, delivering and monitoring the treatment is rapidly expanding to include new fields such as quantitative morphological and functional imaging and the possibility of individually predicting outcome and toxicity. The pivotal position of medical physicists in treatment personalization probably represents the main challenge of current and next years and needs a gradual change of vision and training, without losing the traditional and fundamental role of physicists to guarantee a high quality of the treatment. The current focus issue is intended to cover traditional and new fields of investigation in prostate cancer radiation therapy with the aim to provide up-to-date reference material to medical physicists daily working to cure prostate cancer patients. The papers presented in this focus issue touch upon present and upcoming challenges that need to be met in order to further advance prostate cancer radiation therapy. We suggest that there is a smart future for medical physicists willing to perform research and innovate, while they continue to provide high-quality clinical service. However, physicists are increasingly expected to actively integrate their implicitly translational, flexible and high-level skills within multi-disciplinary teams including many clinical figures (first of all radiation oncologists) as well as scientists from other disciplines.