Dynamic Prediction of Toxicities in Head and Neck Cancer Radiotherapy by 3D Convolutional Neural Network Using Daily Cone-Beam CTs

PURPOSE/OBJECTIVE(S)

Radiotherapy (RT) is essential in head and neck cancer (HNC) treatments, but often causes significant toxicity. Different machine learning models have shown promise in predicting RT-induced toxicity, but none have yet integrated the fluctuating anatomical changes. By integrating daily cone-beam CTs (CBCT) allowing sequential anatomical views, our aim is to build a dynamic predictive model for three major HNC RT toxicities: reactive feeding tube placement, hospitalization and radionecrosis (RN).

MATERIALS/METHODS

292 HNC cases treated with curative RT between 2017 and 2019 at our institution were retrospectively analyzed for clinical and radiological data. VoxelMorph, a deep deformable registration model, integrated the daily anatomical deformations between each CBCT and the planning CT, then converted them to Jacobian determinant matrix (Jf). Resnet, a convolutional neural network with multiple layers was trained using a 5-fold cross validation to integrate both radiological and clinical data. Each toxicity was classified as a binary decision using the cross-entropy loss to account for a class imbalance. Its predictive performance was compared to the baseline model using only clinical data.

RESULTS

The cohort included 78% men and 22% women, with a median age of 63 years (range 35-84). Primary cancer sites were 46% oropharynx, 19% larynx, 14% oral cavity, 7.5% nasopharynx, 5% hypopharynx, 4% unknown primary and 5% others; and stage ranged between Tx-4b N0 and 3b M0 (AJCC 8th Ed). Induction chemotherapy, concurrent chemotherapy, and adjuvant RT was used in 9%, 57% and 20% of patients, respectively. The incidence of feeding tube, hospitalization and RN was 19.9%, 7.2%, and 3.8%, respectively. Integrating Jf from the 10th RT CBCT showed better accuracy for each toxicity prediction: feeding tube (69.1% > 57.2%), hospitalization (75.3% > 63.1%) and RN (85.8% > 75.7%). Integrating both the raw CBCT and Jf improved hospitalization prediction (79.0% > 73.6%). Substituting Jf for the raw CBCT improved the prediction for RN (79.7% > 74.7%) and hospitalization (73.6% > 64.4%). For feeding tube, predictive performance of the Jf model trained against deformations showed a positive correlation between its performance and the RT received (r2 > 0.9) with increasing RT fractions, with a maximum accuracy of 83.1% at the 25th fraction. No such correlation was found for RN or hospitalization prediction.

CONCLUSION

To our knowledge, this is the first study showing promising results to predict HNC RT toxicities using daily per-treatment CBCT. Next steps involve integrating both the radiomic and the dosimetric inputs to build a more powerful model. This could expand to predict therapeutic outcomes and, ultimately, could guide decisions in individualized RT.

Technical note: Consistency of IAEA's TRS-483 and AAPM's extended TG-51 protocols for clinical reference dosimetry of the CyberKnife M6 machine

BACKGROUND

While IAEA's TRS-483 code of practice is adapted for the calibration of CyberKnife machines, AAPM's TG-51 is still the protocol recommended by the manufacturer for their calibration. The differences between both protocols could lead to differences in absorbed dose to water during the calibration process.

PURPOSE

The aims of this work are to evaluate the difference resulting from the application of TG-51 (including the manufacturer's adaptations) and TRS-483 in terms of absorbed dose to water for a CyberKnife M6, and to evaluate the consistency of TRS-483.

METHODS

Measurements are performed on a CyberKnife M6 unit under machine-specific reference conditions using a calibrated Exradin A12 ionization chamber. Monte Carlo (MC) simulations are performed to estimate k Q msr , Q 0 f msr , f ref $k_{Q_{\mathrm{msr}},Q_0}^{f_{\mathrm{msr}},f_{\mathrm{ref}}}$ and k vol $k_{\text{vol}}$ using a fully modeled detector and an optimized CyberKnife M6 beam model. The latter is also estimated experimentally. Differences between the adapted TG-51 and TRS-483 protocols are identified and their impact is quantified.

RESULTS

When using an in-house experimentally-evaluated volume averaging correction factor, a difference of 0.11% in terms of absorbed dose to water per monitor unit is observed when applying both protocols. This disparity is solely associated to the difference in beam quality correction factor. If a generic volume averaging correction factor is used during the application of TRS-483, the difference in calibration increases to 0.14%. In both cases, the disparity is not statistically significant according to TRS-483's reported uncertainties on their beam quality correction factor (i.e., 1%). MC results lead to k Q msr , Q 0 f msr , f ref = 1.0004 ± 0.0002 $k_{Q_{\mathrm{msr}},Q_0}^{f_{\mathrm{msr}},f_{\mathrm{ref}}}=1.0004\pm 0.0002$ and k vol = 1.0072 ± 0.0009 $k_{\text{vol}}=1.0072\pm 0.0009$ . Results illustrate that the generic beam quality correction factor provided in the TRS-483 might be overestimated by 0.36% compared to our specific model and that this overestimation could be due to the volume averaging component.

CONCLUSIONS

For clinical reference dosimetry of the CyberKnife M6, the application of TRS-483 is found to be consistent with TG-51.

RADT-10. THE LOST METASTASES: DEEP LEARNING’S POTENTIAL IN RADIOSURGERY QUALITY ASSURANCE

Introduction

Identifying, segmenting, measuring, and following multiple brain metastases treated with radiosurgery can be time consuming and error prone. Machine learning has shown promise for automated detection and segmentation. Recently, a U-Net inspired model combining volume aware loss functions and volume aware sampling methods was trained in an industrial-academic partnership. A total of 530 clinically annotated T1 gadolinium MRIs were used. Initial validation showed a high sensitivity (91%) with an average of 0.66 false positives per MRI. The goal of the present work was to characterize those “false positives” which may represent clinically undetected metastases.

METHODS

The images used for model development were clinically annotated for radiosurgery planning. Lesions had first been identified by a radiologist, second by clinicians during tumor board review, third by the treating radiation oncologist and the treating neurosurgeon (potentially after segmentation by a trainee) and finally by fellow radiation oncologists during quality assurance rounds. Despite these multiple checks, 10 patients (2%) had brain lesions considered potential clinical misses when all “false positives” were manually reviewed by a single investigator. Further detailed review including prior and subsequent imaging was used to arbitrate the nature of these lesions.

RESULTS

Among the 10 cases, four were confirmed as undetected metastases: two lesions required subsequent radiosurgery and 2 patients died prior to further imaging. The six other lesions were adjudicated as true “false positives” (typically vascular).

CONCLUSION

The multi-tier radiosurgery workflow at our institution left very few unidentified brain metastases (0.8%). Despite this low error rate, our AI algorithm still detected two lesions that required further treatment. Future investigations will focus on potential roles of AI in simplifying and accelerating our workflow. It also remains to be established if more undetected metastases would be seen in community settings where workflows include fewer sequential imaging reviews.

Efficient dose-rate correction of silicon diode relative dose measurements

PURPOSE

Silicon diodes are often the detector of choice for relative dose measurements, particularly in the context of radiotherapy involving small photon beams. However, a major drawback lies in their dose-rate dependency. Although ionization chambers are often too large for small field output factor measurements, they are valuable instruments to provide reliable percent-depth dose curves in reference beams. The aim of this work is to propose a practical and accurate method for the characterization of silicon diode dose-rate dependence correction factors using ionization chamber measurements as a reference.

METHODS

The robustness of ionization chambers for percent-depth dose measurements is used to quantify the dose-rate dependency of a diode detector. A mathematical formalism, which exploits the error induced in percent-depth ionization curves for diodes by their dose-rate dependency, is developed to derive a dose-rate correction factor applicable to diode relative measurements. The method is based on the definition of the recombination correction factor given in the addendum to TG 51 and is applied to experimental measurements performed on a CyberKnife M6 radiotherapy unit using a PTW 60012 diode detector. A measurement-based validation is provided by comparing corrected percent-depth ionization curves to measurements performed with a PTW 60019 diamond detector which does not exhibit dose-rate dependence.

RESULTS

Results of dose-rate correction factors for percent-depth ionization curves, off-axis ratios, tissue-phantom ratios and small field output factors are coherent with the expected behavior of silicon diode detectors. For all considered setups and field sizes, the maximum correction and the maximum impact of the uncertainties induced by the correction are obtained for off-axis ratios for the 60 mm collimator, with a correction of 2.5% and an uncertainty of 0.34%. For output factors, corrections range from 0.33% to 0.82% for all field sizes considered, and increase with the reduction of the field size. Comparison of percent-depth ionization curves corrected for dose-rate and for in-depth beam quality variations illustrate excellent agreement with measurements performed using the diamond detector.

CONCLUSIONS

The proposed method allows the efficient and precise correction of the dose-rate dependence of silicon diode detectors in the context of clinical relative dosimetry. This article is protected by copyright. All rights reserved.

A probabilistic approach for determining Monte Carlo beam source parameters: II. Impact of beam modeling uncertainties on dosimetric functions and treatment plans

Objective. The Monte Carlo method is recognized as a valid approach for the evaluation of dosimetric functions for clinical use. This procedure requires the accurate modeling of the considered linear accelerator. In Part I, we propose a new method to extract the probability density function of the beam model physical parameters. The aim of this work is to evaluate the impact of beam modeling uncertainties on Monte Carlo evaluated dosimetric functions and treatment plans in the context of small fields. Approach. Simulations of output factors, output correction factors, dose profiles, percent-depth doses and treatment plans are performed using the CyberKnife M6 model developed in Part I. The optimized pair of electron beam energy and spot size, and eight additional pairs of beam parameters representing a 95% confidence region are used to propagate the uncertainties associated to the source parameters to the dosimetric functions. Main results. For output factors, the impact of beam modeling uncertainties increases with the reduction of the field size and confidence interval half widths reach 1.8% for the 5 mm collimator. The impact on output correction factors cancels in part, leading to a maximum confidence interval half width of 0.44%. The impact is less significant for percent-depth doses in comparison to dose profiles. For these types of measurement, in absolute terms and in comparison to the reference dose, confidence interval half widths less than or equal to 1.4% are observed. For simulated treatment plans, the impact is more significant for the treatment delivered with a smaller field size with confidence interval half widths reaching 2.5% and 1.4% for the 5 and 20 mm collimators, respectively. Significance. Results confirm that AAPM TG-157's tolerances cannot apply to the field sizes studied. This study provides an insight on the reachable dose calculation accuracy in a clinical setup.

A probabilistic approach for determining Monte Carlo beam source parameters: I. Modeling of a CyberKnife M6 unit

Objective. During Monte Carlo modeling of external radiotherapy beams, models must be adjusted to reproduce the experimental measurements of the linear accelerator being considered. The aim of this work is to propose a new method for the determination of the energy and spot size of the electron beam incident on the target of a linear accelerator using a maximum likelihood estimation. Approach. For that purpose, the method introduced by Francescon et al (2008 Med. Phys. 35 504–13) is expanded upon in this work. Simulated tissue-phantom ratios and uncorrected output factors using a set of different detector models are compared to experimental measurements. A probabilistic formalism is developed and a complete uncertainty budget, which includes a detailed simulation of positioning errors, is evaluated. The method is applied to a CyberKnife M6 unit using four detectors (PTW 60012, PTW 60019, Exradin A1SL and IBA CC04), with simulations being performed using the EGSnrc suite. Main results. The likelihood distributions of the electron beam energy and spot size are evaluated, leading to E ˆ = 7.42 ± 0.17 MeV and F ˆ = 2.15 ± 0.06 mm . Using these results and a 95% confidence region, simulations reproduce measurements in 13 out of the 14 considered setups. Significance. The proposed method allows an accurate beam parameter optimization and uncertainty evaluation during the Monte Carlo modeling of a radiotherapy unit.

Dosimetric verification of lung phantom calculated by collapsed cone convolution: A Monte Carlo and experimental evaluation

OBJECTIVE

To evaluate the dose calculation accuracy in the Prowess Panther treatment planning system (TPS) using the collapsed cone convolution (CCC) algorithm.

METHODS

The BEAMnrc Monte Carlo (MC) package was used to predict the dose distribution of photon beams produced by the Oncor® linear accelerator (linac). The MC model of an 18 MV photon beam was verified by measurement using a p-type diode dosimeter. Percent depth dose (PDD) and dose profiles were used for comparison based on three field sizes: 5×5, 10×10, and 20×20cm2. The accuracy of the CCC dosimetry was also evaluated using a plan composed of a simple parallel-opposed field (11×16cm2) in a lung phantom comprised of four tissue simulating media namely, lung, soft tissue, bone and spinal cord. The CCC dose calculation accuracy was evaluated by MC simulation and measurements according to the dose difference and 3D gamma analysis. Gamma analysis was carried out through comparison of the Monte Carlo simulation and the TPS calculated dose.

RESULTS

Compared to the dosimetric results measured by the Farmer chamber, the CCC algorithm underestimated dose in the planning target volume (PTV), right lung and lung-tissue interface regions by about -0.11%, -1.6 %, and -2.9%, respectively. Moreover, the CCC algorithm underestimated the dose at the PTV, right lung and lung-tissue interface regions in the order of -0.34%, -0.4% and -3.5%, respectively, when compared to the MC simulation. Gamma analysis results showed that the passing rates within the PTV and heterogeneous region were above 59% and 76%. For the right lung and spinal cord, the passing rates were above 80% for all gamma criteria.

CONCLUSIONS

This study demonstrates that the CCC algorithm has potential to calculate dose with sufficient accuracy for 3D conformal radiotherapy within the thorax where a significant amount of tissue heterogeneity exists.

Novel multimodality segmentation using level sets and Jensen-Rényi divergence

PURPOSE

Positron emission tomography (PET) is playing an increasing role in radiotherapy treatment planning. However, despite progress, robust algorithms for PET and multimodal image segmentation are still lacking, especially if the algorithm were extended to image-guided and adaptive radiotherapy (IGART). This work presents a novel multimodality segmentation algorithm using the Jensen-Rényi divergence (JRD) to evolve the geometric level set contour. The algorithm offers improved noise tolerance which is particularly applicable to segmentation of regions found in PET and cone-beam computed tomography.

METHODS

A steepest gradient ascent optimization method is used in conjunction with the JRD and a level set active contour to iteratively evolve a contour to partition an image based on statistical divergence of the intensity histograms. The algorithm is evaluated using PET scans of pharyngolaryngeal squamous cell carcinoma with the corresponding histological reference. The multimodality extension of the algorithm is evaluated using 22 PET/CT scans of patients with lung carcinoma and a physical phantom scanned under varying image quality conditions.

RESULTS

The average concordance index (CI) of the JRD segmentation of the PET images was 0.56 with an average classification error of 65%. The segmentation of the lung carcinoma images had a maximum diameter relative error of 63%, 19.5%, and 14.8% when using CT, PET, and combined PET/CT images, respectively. The estimated maximal diameters of the gross tumor volume (GTV) showed a high correlation with the macroscopically determined maximal diameters, with a R(2) value of 0.85 and 0.88 using the PET and PET/CT images, respectively. Results from the physical phantom show that the JRD is more robust to image noise compared to mutual information and region growing.

CONCLUSIONS

The JRD has shown improved noise tolerance compared to mutual information for the purpose of PET image segmentation. Presented is a flexible framework for multimodal image segmentation that can incorporate a large number of inputs efficiently for IGART.

WE-E-213CD-08: A Novel Level Set Active Contour Algorithm Using the Jensen-Renyi Divergence for Tumor Segmentation in PET

PURPOSE

Positron emission tomography (PET) presents a valuable resource for delineating the biological tumor volume (BTV) for image-guided radiotherapy. However, accurate and consistent image segmentation is a significant challenge within the context of PET, owing to its low spatial resolution and high levels of noise. Active contour methods based on the level set methods can be sensitive to noise and susceptible to failing in low contrast regions. Therefore, this work evaluates a novel active contour algorithm applied to the task of PET tumor segmentation.

METHODS

A novel active contour segmentation algorithm based on maximizing the Jensen-Renyi Divergence between regions of interest was applied to the task of segmenting lesions in 7 patients with T3-T4 pharyngolaryngeal squamous cell carcinoma. The algorithm was implemented on an NVidia GEFORCE GTV 560M GPU. The cases were taken from the Louvain database, which includes contours of the macroscopically defined BTV drawn using histology of resected tissue. The images were pre-processed using denoising/deconvolution.

RESULTS

The segmented volumes agreed well with the macroscopic contours, with an average concordance index and classification error of 0.6 ± 0.09 and 55 ± 16.5%, respectively. The algorithm in its present implementation requires approximately 0.5-1.3 sec per iteration and can reach convergence within 10-30 iterations.

CONCLUSIONS

The Jensen-Renyi active contour method was shown to come close to and in terms of concordance, outperforms a variety of PET segmentation methods that have been previously evaluated using the same data. Further evaluation on a larger dataset along with performance optimization is necessary before clinical deployment.

The effect of interfraction prostate motion on IMRT plans: a dose-volume histogram analysis using a Gaussian error function model

The Gaussian error function model, containing pairs of error and complementary error functions, was used to carry out cumulative dose-volume histogram (cDVH) analysis on prostate intensity modulated radiation therapy (IMRT) plans with interfraction prostate motion. Cumulative DVHs for clinical target volumes (CTVs) shifted in the anterior-posterior directions based on a 7-beam IMRT plan were calculated and modeled using the Pinnacle3 treatment planning system and a Gaussian error function, respectively. As the parameters in the error function model, namely, a, b and c were related to the shape of the cDVH curve, evaluation of cDVHs corresponding to the prostate motion based on the model parameters becomes possible as demonstrated in this study. It was found that deviations of the cDVH for the CTV were significant, when the CTV-planning target volume (PTV) margin was underestimated in the anterior-posterior directions, particularly in the posterior direction for a patient with relatively small prostate volume (39 cm3). Analysis of the cDVH for the CTV shifting in the anterior-posterior directions using the error function model showed that parameters a1,2, which were related to the maximum relative volume of the cDVH, changed symmetrically when the prostate was shifted in the anterior and posterior directions. This change was more significant for the larger prostate. For parameters b related to the slope of the cDVH, b1,2 changed symmetrically from the isocenter, when the CTV was within the PTV. This was different from parameters c (c1,2 are related to the maximum dose of the cDVH), which did not vary significantly with the prostate motion in the anterior-posterior directions and prostate volume. Using the patient data, this analysis validates the error function model, and further verified the clinical application of this mathematical model on treatment plan evaluations.

Technical note: dose-volume histogram analysis in radiotherapy using the Gaussian error function

A mathematical model based on the Gaussian error and complementary error functions was proposed to describe the cumulative dose-volume histogram (cDVH) for a region of interest in a radiotherapy plan. Parameters in the model (a, b, c) are related to different characteristics of the shape of a cDVH curve such as the maximum relative volume, slope and position of a curve drop off, respectively. A prostate phantom model containing a prostate, the seminal vesicle, bladder and rectum with cylindrical organ geometries was used to demonstrate the effect of interfraction prostate motion on the cDVH based on this error function model. The prostate phantom model was planned using a five-beam intensity modulated radiotherapy (IMRT), and a four-field box (4FB), technique with the clinical target volume (CTV) shifted in different directions from the center. In the case of the CTV moving out of the planning target volume (PTV), that is, the margin between the CTV and PTV is underestimated, parameter c (related to position of curve drop off) in the 4FB plan and parameters b (related to the slope of curve) and c in the IMRT plan vary significantly with CTV displacement. This shows that variation of the cDVH is present in the 4FB plan and such variation is more serious in the IMRT plan. These variations of cDVHs for 4FB and IMRT are due to the different dose gradients at the CTV edges in the anterior and posterior directions for the 4FB and IMRT plan. It is believed that a mathematical representation of the dose-volume relationship provides another viewpoint from which to illustrate problems with radiotherapy delivery such as internal organ motion that affect the dose distribution in a treatment plan.

Using a GIS transfer model to evaluate pollutant loads in the Lake Kinneret watershed, Israel

Lake Kinneret (Sea of Galilee) is the only large surface water body in Israel, encompassing an area of 167 km2 and supplying some 30% of the country's fresh water. Pollution from anthropogenic sources and water abstraction for domestic and agricultural uses has long been threatening the water quality of the lake. Point-source pollution in the watershed has decreased drastically with the development of wastewater treatment. However, diffuse pollution from agricultural activities is still an unresolved issue. In this paper we present an application of AVGWLF (a GIS-based watershed load model) to the Lake Kinneret watershed. The model allows one to simulate daily stream flows and monthly sediment, nitrogen, and phosphorus loads discharged to the lake from the surrounding watershed. Results from simulations yield a satisfactory correspondence between simulated and measured daily water volume. Partition by source of total phosphorus delivered to the lake in the period of 2000-04 confirms the reduction in point source nutrient contribution due to improvement of wastewater treatment facilities in the area. Future management should focus on reduction of nutrients originating from septic systems (point sources) and pasture and cropland areas (diffuse sources). Results from simulations will enable watershed managers to prioritize effective management alternatives for protecting the water quality in the lake.

Sulfate reduction in Lake Agmon, Israel

Lake Agmon, a newly reflooded water body in the southern part of the Hula valley is characterized by a clear phase period in winter with almost no blooms of phytoplankton. Dense macrobenthos and algal blooms form in the lake during summer and autumn. High primary production and chlorophyll-a concentrations were measured in April and during the summer of 1997. Fresh organic matter in the sediments from the degradation and decomposition of the blooms together with high sulfate concentrations, allowed microbial sulfate reduction to occur. Sediment cores taken from different sites (peat and marl) during various seasons in 1997, showed high sulfate reduction rates in June in the marl region, and in September in the peat region (842 and 2834 nmol SO(4)2- reduced ml(-1) day(-1), respectively). In February, corresponding to the development and decline of macrophytes and algal blooms, lower rates of sulfate reduction were recorded (11 nmol SO(4)2- reduced ml(-1) day(-1)). Sulfate reduction is limited by the supply of organic matter and is one of the major processes contributing to the mineralization of organic matter in this lake.

Complete paraplegia as a result of regional anesthesia

Complications after spinal or epidural anesthesia are rare. We report 2 cases of postoperative, complete paraplegia after regional anesthesia in orthopaedic patients not on anticoagulants. The paralysis was likely the result of spinal cord compression secondary to an epidural hematoma in 1 case and subdural hematoma in 1 case. A review of the literature regarding complications of regional anesthesia is presented. Regional anesthesia should be administered with caution and in selected patients.

Eilatin: a novel marine alkaloid inhibits in vitro proliferation of progenitor cells in chronic myeloid leukemia patients

We examined the effect of Eilatin, a novel marine product, on the survival of human myeloid progenitor cells (CFU-C) isolated from normal individuals and from 12 patients with Philadelphia chromosome-positive (Ph+) chronic myelogenous leukemia (CML) in chronic phase and blastic crisis. We compared its effect to the effect of interferon-alpha (IFN-alpha) and cytosine arabinoside (Ara-C). Eilatin, IFN-alpha, and Ara-C inhibited the proliferation of CFU-C from normal individuals and CML patients in a dose-dependent manner. The percent survival of colony-forming units from bone marrow (BM) of seven CML patients in chronic phase exposed for 16 hours to Eilatin (10(-7) and 10(-6) M), IFN-alpha (500 U/mL), or Ara-C (10(-9) M and 10(-8) M) was found to be statistically lower (p < 0.05) than the percent survival of myeloid progenitors from normal individuals. A 16-hour exposure of CD34+ cells isolated from peripheral blood (PB) of three CML patients in blastic crisis and from BM of two patients in chronic phase to Eilatin 10(-7) M, IFN-alpha 500 U/mL, Ara-C 10(-9) M resulted in a marked inhibition in the ability of the cells to proliferate in liquid culture and a reduction in CFU-C content. Using fluorescent in situ hybridization (FISH), we evaluated detection of the BCR/ABL fusion product in the CD34+ cells. All five patients were 100% Ph+ at diagnosis. BCR/ABL translocations were detected in 94.6 +/- 0.6% of CD34+ cells after growth in liquid culture for 7 days. The level of BCR/ABL fusion signals detected after exposure of CD34+ cells for 16 hours to Eilatin 10(-7) M, IFN-alpha 500 U/mL, or Ara-C 10(-9) M were 54.5 +/- 5%, 63.6 +/- 5%, and 70 +/- 4%, respectively (mean +/- SE, n = 5). Our data indicate that Eilatin, a substance isolated from the Red Sea purple tunicate Eudistoma sp., has an antileukemic effect against in vitro Ph+ cells and may be used in conjunction with currently available agents for ex vivo purging of BM and/or PB of CML patients in conjunction with autologous bone marrow transplantation.

Positron emission tomographic scan investigations of Huntington's disease: cerebral metabolic correlates of cognitive function

Fifteen drug-free patients with early to mid-stage Huntington's disease (HD) were evaluated with positron emission tomographic (PET) scans of 18F-2-fluoro-2-deoxy-D-glucose uptake and quantitative measures of neurological function, learning, memory, and general intelligence. In comparison with a group of normal volunteers, the HD patients showed lower metabolism in both caudate (p less than 0.001) and putamen (p less than 0.001) on PET scans. A significant and positive relationship was found between neuropsychological measures of verbal learning and memory and caudate metabolism in the patient group but not in the normal group. Visual-spatial learning did not reflect a similar pattern, but performance intelligence quotient was positively related to both caudate and putamen metabolism in the HD group. Vocabulary level was unrelated to either brain structure. Discussion focuses on these and other observed brain-behavior relationships and on the implications of these findings for general behaviors such as those involved in coping and adaptation.

Normal caudate glucose metabolism in persons at risk for Huntington's disease

Glucose metabolism was examined by positron emission tomographic scanning with F-2-fluoro-2-deoxy-D-glucose in 29 persons at risk for Huntington's disease (HD), 28 age-matched controls, nine patients with stage I, and eight patients with stage II symptomatic HD. Absolute caudate metabolic rates and normalized indexes of caudate metabolism for at-risk persons were normal compared with controls. No at-risk person had caudate indexes outside two SDs of the controls' mean. Caudate metabolism in the earliest HD cases was significantly reduced compared with controls and at-risk persons, but within the 99% confidence levels of both groups. Stage II patients had caudate measures that were significantly depressed compared with those of stage I HD patients. Measurement of caudate glucose hypometabolism is unlikely to be sufficiently sensitive to serve as a presymptomatic marker of heterozygote status, although it will provide a sensitive marker for progressive caudate dysfunction in HD.

Genetic analysis of thirty-three platelet polypeptides detected in two-dimensional polyacrylamide gels

Two-dimensional gel electrophoresis followed by silver-staining was utilized to visualize platelet polypeptides for genetic analysis. A subset of 33 polypeptides that were most suited for scoring was selected. Families consisting of father-mother-child trios were studied. Thirty-six polypeptides of a total of 1,413 scored in children's gels exhibited the combination of a normal and a variant polypeptide. The observed index of heterozygosity of 2.55% is comparable to our previously reported findings for red cell proteins.

Nightmares: clinical characteristics and personality patterns

The authors evaluated the development and clinical course of nightmares and the personality patterns of people with this dosorder by studying 30 adults who had a current complaint of nightmares. Nightmares usually began in childhood or adolescence and were chronic. Psychological factors played a major role in the development and persistence of nightmares, major life events were often associated with the onset of the disorder, mental stress usually increased the frequency of events, and psychological testing indicated relatively high levels of psychopathology. Nightmares sufferers in general were distrusful, alienated, sufferers in general were distrustful, alienated, and emotionally estranged, and many showed a chronic schizoid pattern of adjustment, but they were not overtly psychotic.