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Abstract Non-small-cell lung cancer (NSCLC) represents approximately 80–85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.more » « less
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Abstract Purpose The radiobiological benefits afforded by spatially fractionated (GRID) radiation therapy pairs well with the dosimetric advantages of proton therapy. Inspired by the emergence of energy‐layer specific collimators in pencil beam scanning (PBS), this work investigates how the spot spacing and collimation can be optimized to maximize the therapeutic gains of a GRID treatment while demonstrating the integration of a dynamic collimation system (DCS) within a commercial beamline to deliver GRID treatments and experimentally benchmark Monte Carlo calculation methods.
Methods GRID profiles were experimentally benchmarked using a clinical DCS prototype that was mounted to the nozzle of the IBA‐dedicated nozzle system. Integral depth dose (IDD) curves and lateral profiles were measured for uncollimated and GRID‐collimated beamlets. A library of collimated GRID dose distributions were simulated by placing beamlets within a specified uniform grid and weighting the beamlets to achieve a volume‐averaged tumor cell survival equivalent to an open field delivery. The healthy tissue sparing afforded by the GRID distribution was then estimated across a range of spot spacings and collimation widths, which were later optimized based on the radiosensitivity of the tumor cell line and the nominal spot size of the PBS system. This was accomplished by using validated models of the IBA universal and dedicated nozzles.
Results Excellent agreement was observed between the measured and simulated profiles. The IDDs matched above 98.7% when analyzed using a 1%/1‐mm gamma criterion with some minor deviation observed near the Bragg peak for higher beamlet energies. Lateral profile distributions predicted using Monte Carlo methods agreed well with the measured profiles; a gamma passing rate of 95% or higher was observed for all in‐depth profiles examined using a 3%/2‐mm criteria. Additional collimation was shown to improve PBS GRID treatments by sharpening the lateral penumbra of the beamlets but creates a trade‐off between enhancing the valley‐to‐peak ratio of the GRID delivery and the dose‐volume effect. The optimal collimation width and spot spacing changed as a function of the tumor cell radiosensitivity, dose, and spot size. In general, a spot spacing below 2.0 cm with a collimation less than 1.0 cm provided a superior dose distribution among the specific cases studied.
Conclusions The ability to customize a GRID dose distribution using different collimation sizes and spot spacings is a useful advantage, especially to maximize the overall therapeutic benefit. In this regard, the capabilities of the DCS, and perhaps alternative dynamic collimators, can be used to enhance GRID treatments. Physical dose models calculated using Monte Carlo methods were experimentally benchmarked in water and were found to accurately predict the respective dose distributions of uncollimated and DCS‐collimated GRID profiles.
