John H. Lewis

Spatial Localization of the K+ Channel Selectivity Filter by Mutant Cycle–Based Structure Analysis

The structurally well-characterized scorpion toxin Agitoxin2 inhibits ion permeation through Shaker K+ channels by binding to the external pore entryway. Scanning mutagenesis identified a set of inhibitor residues critical for making energetic contacts with the channel. Using thermodynamic mutant cycle analysis, we have mapped channel residues relative to the known inhibitor structure. This study constrains the position of multiple channel residues within the pore-forming loops; in one stretch, we have been able to map five out of seven contiguous residues to the inhibitor interaction surface, including those involved in ion selectivity. One interaction in particular, that of K27M on the inhibitor with Y445F on the channel, is unique in that it depends on the K+ ion concentration. These results reveal a shallow vestibule formed by the pore loops at the K+ channel entryway. The selectivity filter is located at the center of the vestibule close to (approximately 5 A) the extracellular solution.

Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

PURPOSE To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. METHODS Given a set of volumetric images of a patient at N breathing phases as the training data, deformable image registration was performed between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, new DVFs can be generated, which, when applied on the reference image, lead to new volumetric images. A volumetric image can then be reconstructed from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. The algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. The training data were generated using a realistic and dynamic mathematical phantom with ten breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. RESULTS The average relative image intensity error of the reconstructed volumetric images is 6.9%±2.4%. The average 3D tumor localization error is 0.8±0.5mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 s (range: 0.17 and 0.35 s). CONCLUSIONS The authors have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.

4D CT sorting based on patient internal anatomy

Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95+/-0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68+/-0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.

Markerless lung tumor tracking and trajectory reconstruction using rotational cone-beam projections: a feasibility study

Algorithms for direct tumor tracking in rotational cone-beam projections and for reconstruction of phase-binned 3D tumor trajectories were developed. The feasibility of the algorithm was demonstrated on a digital phantom, a physical phantom and two patients. Tracking results were obtained by comparing reference templates generated from 4DCT to rotational cone-beam projections. The 95th percentile absolute errors (e(95)) in phantom tracking results did not exceed 1.7 mm in either imager dimension, while e(95) in the patients was 3.3 mm or less. Accurate phase-binned trajectories were reconstructed in each case, with 3D maximum errors of no more than 1.0 mm in the phantoms and 2.0 mm in the patients. This work shows the feasibility of a direct tumor tracking technique for rotational images, and demonstrates that an accurate 3D tumor trajectory can be reconstructed from relatively less accurate tracking results. The ability to reconstruct the tumors average trajectory from a 3D cone-beam CT scan on the day of treatment could allow for better patient setup and quality assurance, while direct tumor tracking in rotational projections could be clinically useful for rotational therapy such as volumetric modulated arc therapy (VMAT).

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.

Lymphocyte transformation in mixed leukocyte cultures in women with normal pregnancy or tumors of placental origin. A preliminary report.

Abstract Lymphocyte transformation in mixed leukocyte cultures has been studied as an in vitro measure of the immunologic response of 10 pregnant women to cellular antigens of their husbands and unrelated males. Similar studies were carried out in 5 women with metastatic choriocarcinoma and 2 with nonmetastatic hydatidiform mole. Most pregnant women show a specific lack of responsiveness to their husbands leukocytes but do not show this in relation to the cells of unrelated males. Studies in patients with trophoblastic neoplasms did not indicate this unresponsiveness to paternal antigens but the data are insufficient to interpret any more of a pattern than this. The pertinence of this type of study to the problem of tolerance of pregnancy as a homograft is discussed.

Daily Online Cone Beam Computed Tomography to Assess Interfractional Motion in Patients With Intact Cervical Cancer

PURPOSE To quantify interfraction motion in patients with intact cervical cancer and assess implications for clinical target volume (CTV) coverage and required planning margins. METHODS AND MATERIALS We analyzed 10 patients undergoing external beam radiotherapy using online cone beam computed tomography (CBCT) before each fraction. CTVs were contoured on the planning CT and on each CBCT. Each CBCT was rigidly registered to the planning CT with respect to bony anatomy. The CTV from each CBCT was projected onto the planning CT and compared to the CTV from the planning CT. Uniform three-dimensional expansions were applied to the planning CTV to assess required planning margins. For each fraction, the minimum margin required to encompass the CTV was calculated, and the volume of CTV (on the CBCT) encompassed by the PTV was determined as a function of margin size. RESULTS A uniform CTV planning treatment volume margin of 15 mm would have failed to encompass the CTV in 32% of fractions. The mean volume of CTV missed, however, was small (4 cc). The mean planning margin (across patients and fractions) required to encompass the CTV was 15 mm. Variation in margin estimates was high, with interpatient variation being the predominant component. Increased rectal volume was associated with posterior (p < 0.0001) and superior (p = 0.0004) shifts in the CTV, whereas increased bladder volume was associated with superior shifts (p < 0.0001). CONCLUSIONS Interfraction motion results in a high probability of missing the CTV using conventional planning margins, but the volume of CTV missed is small. Adaptive radiotherapy approaches are needed to improve treatment accuracy.

On a PCA-based lung motion model

Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach.

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

PURPOSE To evaluate an algorithm for real-time 3D tumor localization from a single x-ray projection image for lung cancer radiotherapy. METHODS Recently, we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection [Li et al., Med. Phys. 37, 2822-2826 (2010)]. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency of using this algorithm for 3D tumor localization were then evaluated on (1) a digital respiratory phantom, (2) a physical respiratory phantom, and (3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. RESULTS For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm which does not seem to be affected by amplitude change, period change, or baseline shift. On an NVIDIA Tesla C1060 graphic processing unit (GPU) card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 s, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 s on the same graphic processing unit (GPU) card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 s. CONCLUSIONS Through a comprehensive evaluation of our algorithm, we have established its accuracy in 3D tumor localization to be on the order of 1 mm on average and 2 mm at 95 percentile for both digital and physical phantoms, and within 2 mm on average and 4 mm at 95 percentile for lung cancer patients. The results also indicate that the accuracy is not affected by the breathing pattern, be it regular or irregular. High computational efficiency can be achieved on GPU, requiring 0.1-0.3 s for each x-ray projection.

Treatment of trophoblastic disease: With rationale for the use of adjunctive chemotherapy at the time of indicated operation☆☆☆

Abstract The results of therapy in 16 patients with metastatic or nonmetastatic trophoblastic disease have been reported. Therapy consisted of chemotherapy alone or chemotherapy with indicated surgery carried out in the middle of a course of drug administration. Agents used were Methotrexate and actinomycin D. A complete remission rate of 75 per cent among 8 patients with metastatic trophoblastic disease and 100 per cent among 8 patients with nonmetastatic disease is recorded. The rationale and feasibility of operating during a course of “adjunctive” chemotherapy are discussed. In this series there was no evidence of delay in wound healing nor spread of disease following the 13 surgical procedures carried out under cover of chemotherapy in 11 of this group of 16 patients. No claim is made in regard to the relative efficacy of this type of combined therapy and chemotherapy alone.