Comparison of Otsu and an adapted Chan-Vese method to determine thyroid active volume using Monte Carlo generated SPECT images.

BACKGROUND: The Otsu method and the Chan-Vese model are two methods proven to perform well in determining volumes of different organs and specific tissue fractions. This study aimed to compare the performance of the two methods regarding segmentation of active thyroid gland volumes, reflecting different clinical settings by varying the parameters: gland size, gland activity concentration, background activity concentration and gland activity concentration heterogeneity. METHODS: A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu's threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient. RESULTS: Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]). CONCLUSIONS: The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.

Improvements of 177Lu SPECT images from sparsely acquired projections by reconstruction with deep-learning-generated synthetic projections.

BACKGROUND: For dosimetry, the demand for whole-body SPECT/CT imaging, which require long acquisition durations with dual-head Anger cameras, is increasing. Here we evaluated sparsely acquired projections and assessed whether the addition of deep-learning-generated synthetic intermediate projections (SIPs) could improve the image quality while preserving dosimetric accuracy. METHODS: This study included 16 patients treated with 177Lu-DOTATATE with SPECT/CT imaging (120 projections, 120P) at four time points. Deep neural networks (CUSIPs) were designed and trained to compile 90 SIPs from 30 acquired projections (30P). The 120P, 30P, and three different CUSIP sets (30P + 90 SIPs) were reconstructed using Monte Carlo-based OSEM reconstruction (yielding 120P_rec, 30P_rec, and CUSIP_recs). The noise levels were visually compared. Quantitative measures of normalised root mean square error, normalised mean absolute error, peak signal-to-noise ratio, and structural similarity were evaluated, and kidney and bone marrow absorbed doses were estimated for each reconstruction set. RESULTS: The use of SIPs visually improved noise levels. All quantitative measures demonstrated high similarity between CUSIP sets and 120P. Linear regression showed nearly perfect concordance of the kidney and bone marrow absorbed doses for all reconstruction sets, compared to the doses of 120P_rec (R2 ≥ 0.97). Compared to 120P_rec, the mean relative difference in kidney absorbed dose, for all reconstruction sets, was within 3%. For bone marrow absorbed doses, there was a higher dissipation in relative differences, and CUSIP_recs outperformed 30P_rec in mean relative difference (within 4% compared to 9%). Kidney and bone marrow absorbed doses for 30P_rec were statistically significantly different from those of 120_rec, as opposed to the absorbed doses of the best performing CUSIP_rec, where no statistically significant difference was found. CONCLUSION: When performing SPECT/CT reconstruction, the use of SIPs can substantially reduce acquisition durations in SPECT/CT imaging, enabling acquisition of multiple fields of view of high image quality with satisfactory dosimetric accuracy.

Persistent malignant stem cells in del(5q) myelodysplasia in remission.

BACKGROUND: The in vivo clinical significance of malignant stem cells remains unclear. METHODS: Patients who have the 5q deletion (del[5q]) myelodysplastic syndrome (interstitial deletions involving the long arm of chromosome 5) have complete clinical and cytogenetic remissions in response to lenalidomide treatment, but they often have relapse. To determine whether the persistence of rare but distinct malignant stem cells accounts for such relapses, we examined bone marrow specimens obtained from seven patients with the del(5q) myelodysplastic syndrome who became transfusion-independent while receiving lenalidomide treatment and entered cytogenetic remission. RESULTS: Virtually all CD34+, CD38+ progenitor cells and stem cells that were positive for CD34 and CD90, with undetectable or low CD38 (CD38−/low), had the 5q deletion before treatment. Although lenalidomide efficiently reduced these progenitors in patients in complete remission, a larger fraction of the minor, quiescent, CD34+,CD38-/low, CD90+ del(5q) stem cells as well as functionally defined del(5q) stem cells remained distinctly resistant to lenalidomide. Over time, lenalidomide resistance developed in most of the patients in partial and complete remission, with recurrence or expansion of the del(5q) clone and clinical and cytogenetic progression. CONCLUSIONS: In these patients with the del(5q) myelodysplastic syndrome, we identified rare and phenotypically distinct del(5q) myelodysplastic syndrome stem cells that were also selectively resistant to therapeutic targeting at the time of complete clinical and cytogenetic remission. (Funded by the EuroCancerStemCell Consortium and others.)

Evaluation of reconstruction methods and image noise levels concerning visual assessment of simulated liver lesions in 111In-octreotide SPECT imaging.

BACKGROUND: Early cancer detection is crucial for patients' survival. The image quality in 111In-octreotide SPECT imaging could be improved by using Monte Carlo (MC)-based reconstruction. The aim of this observational study was to determine the detection rate of simulated liver lesions for MC-based ordered subset expectation maximization (OSEM) reconstruction compared to conventional attenuation-corrected OSEM reconstruction. METHODS: Thirty-seven SPECT/CT examinations with 111In-octreotide were randomly selected. The inclusion criterion was no liver lesions at the time of examination and for the following 3 years. SPECT images of spheres representing lesions were simulated using MC. The raw data of the spheres were added to the raw data of the established healthy patients in 26 of the examinations, and the remaining 11 examinations were not modified. The images were reconstructed using conventional OSEM reconstruction with attenuation correction and post filtering (fAC OSEM) and MC-based OSEM reconstruction without and with post filtering (MC OSEM and fMC OSEM, respectively). The images were visually and blindly evaluated by a nuclear medicine specialist. The criteria evaluated were liver lesion yes or no, including coordinates if yes, with confidence level 1-3. The percentage of detected lesions and accuracy (percentage of correctly classified cases), as well as tumor-to-normal tissue concentration (TNC) ratios and signal-to-noise ratios (SNRs), were evaluated. RESULTS: The detection rates were 30.8% for fAC OSEM, 42.3% for fMC OSEM, and 50.0% for MC OSEM. The accuracies were 45.9% for fAC OSEM, 45.9% for fMC OSEM, and 54.1% for MC OSEM. The number of false positives was higher for fMC and MC OSEM. The observer's confidence level was higher in filtered images than in unfiltered images. TNC ratios were significantly higher, statistically, with MC OSEM and fMC OSEM than with AC OSEM, but SNRs were similar due to higher noise with MC OSEM. CONCLUSION: One in two lesions were found using MC OSEM versus one in three using conventional reconstruction. TNC ratios were significantly improved, statistically, using MC-based reconstruction, but the noise levels increased and consequently the confidence level of the observer decreased. For further improvements, image noise needs to be suppressed.

ACTIVITY CONCENTRATION ESTIMATION IN AUTOMATED KIDNEY SEGMENTATION BASED ON CONVOLUTION NEURAL NETWORK METHOD FOR 177LU-SPECT/CT KIDNEY DOSIMETRY.

For 177Lu-DOTATATE treatments, dosimetry based on manual kidney segmentation from computed tomography (CT) is accurate but time consuming and might be affected by misregistration between CT and SPECT images. This study develops a convolution neural network (CNN) for automated kidney segmentation that accurately aligns CT segmented volume of interest (VOI) to the kidneys in SPECT images. The CNN was trained with SPECT/CT images performed over the abdominal area of 137 patients treated with 177Lu-DOTATATE. Activity concentrations in automated and manual segmentations were strongly correlated for both kidneys (r > 0.96, p < 0.01) in the testing cohort (n = 20). The Bland-Altman analyses demonstrated higher accuracy for the CNN segmentation compared to the manual segmented kidneys without VOI adjustment. The CNN demonstrated a potential for accurate kidney segmentation. The CNN was a fast and robust approach for assessment of activity concentrations in SPECT images, and performed equally well as the manual segmentation method.

Deep-Learning Generation of Synthetic Intermediate Projections Improves 177Lu SPECT Images Reconstructed with Sparsely Acquired Projections.

The aims of this study were to decrease the 177Lu-SPECT acquisition time by reducing the number of projections and to circumvent image degradation by adding deep-learning-generated synthesized projections. Methods: We constructed a deep convolutional U-net-shaped neural network for generation of synthetic intermediate projections (CUSIPs). The number of SPECT investigations was 352 for training, 37 for validation, and 15 for testing. The input was every fourth projection of 120 acquired SPECT projections, that is, 30 projections. The output was 30 synthetic intermediate projections (SIPs) per CUSIP. SPECT images were reconstructed with 120 or 30 projections, or with 120 projections when 90 SIPs were generated from 30 projections (30-120SIPs), using 3 CUSIPs. The reconstructions were performed with 2 ordered-subset expectation maximization (OSEM) algorithms: attenuation-corrected (AC) OSEM, and attenuation, scatter, and collimator response-corrected (ASCC) OSEM. The quality of the SIPs and SPECT images was quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index metrics. From a Jaszczak SPECT phantom, the recovery and signal-to-noise ratio (SNR) were determined. In addition, an experienced observer qualitatively assessed the SPECT image quality of the test set. Kidney activity concentrations, as determined from the different SPECT images, were compared. Results: The generated SIPs had a mean SSIM value of 0.926 (SD, 0.061). For AC-OSEM, the reconstruction with 30-120SIPs had higher SSIM (0.993 vs. 0.989, P < 0.001) and PSNR (49.5 vs. 47.2, P < 0.001) values than the reconstruction with 30 projections. ASCC-OSEM had higher SSIM and PSNR values than AC-OSEM (P < 0.001). There was a minor loss in recovery for 30-120SIPs, but SNR was clearly improved compared with 30 projections. The observer assessed 27 of 30 images reconstructed with 30 projections as having unacceptable noise levels, whereas the corresponding values were 2 of 60 for 30-120SIPs and 120 projections. Image quality did not differ significantly between 30-120SIPs and 120 projections. The kidney activity concentration was similar between the different projection sets, excepting a minor reduction of 2.5% for ASCC-OSEM 30-120SIPs. Conclusion: Adopting SIPs for sparsely acquired projections considerably recovers image quality and could allow a reduced SPECT acquisition time in clinical dosimetry protocols.

Optimizing the Schedule of PARP Inhibitors in Combination with 177Lu-DOTATATE: A Dosimetry Rationale.

177Lu-DOTATATE for neuroendocrine tumours is considered a low-toxicity treatment and may therefore be combined with other pharmaceuticals to potentiate its efficacy. One approach is to add a poly-[ADP-ribose]-polymerase (PARP) inhibitor to decrease the ability of tumour cells to repair 177Lu-induced DNA damage. To decrease the risk of side effects, the sequencing should be optimized according to the tumour-to-normal tissue enhanced dose ratio (TNED). The aim of this study was to investigate how to enhance 177Lu-DOTATATE by optimal timing of the addition of a PARP inhibitor. Biokinetic modelling was performed based on the absorbed dose to the bone marrow, kidneys and tumour; determined from SPECT/CT and planar images from 17 patients treated with 177Lu-DOTATATE. To investigate the theoretical enhanced biological effect of a PARP inhibitor during 177Lu-DOTATATE treatment, the concept of relative biological effectiveness (RBE) was used, and PARP inhibitor administration was simulated over different time intervals. The absorbed dose rate for the tumour tissue demonstrated an initial increase phase until 12 h after infusion followed by a slow decrease. In contrast, the bone marrow showed a rapid initial dose rate decrease. Twenty-eight days after infusion of 177Lu-DOTATATE, the full absorbed dose to the bone marrow and kidney was reached. Using an RBE value of 2 for both the tumour and normal tissues, the TNED was increased compared to 177Lu-DOTATATE alone. According to the modelling, the PARP inhibitor should be introduced approximately 24 h after the start of 177Lu-DOTATATE treatment and be continued for up to four weeks to optimize the TNED. Based on these results, a phase I trial assessing the combination of olaparib and 177Lu-DOTATATE in somatostatin receptor-positive tumours was launched in 2020 (NCT04375267).

EVALUATION OF THE SPATIAL RESOLUTION IN MONTE CARLO-BASED SPECT/CT RECONSTRUCTION OF 111IN-OCTREOTIDE IMAGES.

The purpose was to evaluate the spatial resolution in 111In-octreotide single-photom emission computed tomography (SPECT)/computed tomography (CT) imaging following reconstructions with three different ordered subset expectation maximizations (OSEM) reconstruction algorithms; attenuation corrected (AC) OSEM, AC OSEM with resolution recovery (ACRR OSEM) and Monte Carlo-based OSEM reconstruction (MC OSEM). SPECT/CT imaging of a triple line phantom containing 111In in air and water was performed. The spatial resolution, represented by the full width at half maximum (FWHM) of a line profile, was determined for each line, for X and Y direction and for all reconstructions. The mean FWHM was 12.2 mm (±standard deviation [SD] 3.7 mm) for AC OSEM, 9.3 mm (±SD 2.5 mm) for ACRR OSEM and 8.2 mm (±SD 2.0 mm) for MC OSEM. MC-based SPECT/CT reconstruction clearly improves the spatial resolution in 111In-octreotide imaging and since MC simulations can be performed for all photon energies MC OSEM has the potential to improve SPECT/CT imaging overall.

First-in-Humans Application of 161Tb: A Feasibility Study Using 161Tb-DOTATOC.

161Tb has decay properties similar to those of 177Lu but, additionally, emits a substantial number of conversion and Auger electrons. The aim of this study was to apply 161Tb in a clinical setting and to investigate the feasibility of visualizing the physiologic and tumor biodistributions of 161Tb-DOTATOC. Methods:161Tb was shipped from Paul Scherrer Institute, Villigen-PSI, Switzerland, to Zentralklinik Bad Berka, Bad Berka, Germany, where it was used for the radiolabeling of DOTATOC. In 2 separate studies, 596 and 1,300 MBq of 161Tb-DOTATOC were administered to a 35-y-old male patient with a metastatic, well-differentiated, nonfunctional malignant paraganglioma and a 70-y-old male patient with a metastatic, functional neuroendocrine neoplasm of the pancreatic tail, respectively. Whole-body planar γ-scintigraphy images were acquired over a period of several days for dosimetry calculations. SPECT/CT images were reconstructed using a recently established protocol and visually analyzed. Patients were observed for adverse events after the application of 161Tb-DOTATOC. Results: The radiolabeling of DOTATOC with 161Tb was readily achieved with a high radiochemical purity suitable for patient application. Planar images and dosimetry provided the expected time-dependent biodistribution of 161Tb-DOTATOC in the liver, kidneys, spleen, and urinary bladder. SPECT/CT images were of high quality and visualized even small metastases in bones and liver. The application of 161Tb-DOTATOC was well tolerated, and no related adverse events were reported. Conclusion: This study demonstrated the feasibility of imaging even small metastases after the injection of relatively low activities of 161Tb-DOTATOC using γ-scintigraphy and SPECT/CT. On the basis of this essential first step in translating 161Tb to clinics, further efforts will be directed toward the application of 161Tb for therapeutic purposes.

A continuous-index hidden Markov jump process for modeling DNA copy number data.

The number of copies of DNA in human cells can be measured using array comparative genomic hybridization (aCGH), which provides intensity ratios of sample to reference DNA at genomic locations corresponding to probes on a microarray. In the present paper, we devise a statistical model, based on a latent continuous-index Markov jump process, that is aimed to capture certain features of aCGH data, including probes that are unevenly long, unevenly spaced, and overlapping. The model has a continuous state space, with 1 state representing a normal copy number of 2, and the rest of the states being either amplifications or deletions. We adopt a Bayesian approach and apply Markov chain Monte Carlo (MCMC) methods for estimating the parameters and the Markov process. The model can be applied to data from both tiling bacterial artificial chromosome arrays and oligonucleotide arrays. We also compare a model with normal distributed noise to a model with t-distributed noise, showing that the latter is more robust to outliers.

Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier.

BACKGROUND: Genome wide gene expression data is a rich source for the identification of gene signatures suitable for clinical purposes and a number of statistical algorithms have been described for both identification and evaluation of such signatures. Some employed algorithms are fairly complex and hence sensitive to over-fitting whereas others are more simple and straight forward. Here we present a new type of simple algorithm based on ROC analysis and the use of metagenes that we believe will be a good complement to existing algorithms. RESULTS: The basis for the proposed approach is the use of metagenes, instead of collections of individual genes, and a feature selection using AUC values obtained by ROC analysis. Each gene in a data set is assigned an AUC value relative to the tumor class under investigation and the genes are ranked according to these values. Metagenes are then formed by calculating the mean expression level for an increasing number of ranked genes, and the metagene expression value that optimally discriminates tumor classes in the training set is used for classification of new samples. The performance of the metagene is then evaluated using LOOCV and balanced accuracies. CONCLUSIONS: We show that the simple uni-variate gene expression average algorithm performs as well as several alternative algorithms such as discriminant analysis and the more complex approaches such as SVM and neural networks. The R package rocc is freely available at http://cran.r-project.org/web/packages/rocc/index.html.

Deep learning-based digitization of prostate brachytherapy needles in ultrasound images.

PURPOSE: To develop, and evaluate the performance of, a deep learning-based three-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) algorithm aimed at finding needles in ultrasound images used in prostate brachytherapy. METHODS: Transrectal ultrasound (TRUS) image volumes from 1102 treatments were used to create a clinical ground truth (CGT) including 24422 individual needles that had been manually digitized by medical physicists during brachytherapy procedures. A 3D CNN U-net with 128 × 128 × 128 TRUS image volumes as input was trained using 17215 needle examples. Predictions of voxels constituting a needle were combined to yield a 3D linear function describing the localization of each needle in a TRUS volume. Manual and AI digitizations were compared in terms of the root-mean-square distance (RMSD) along each needle, expressed as median and interquartile range (IQR). The method was evaluated on a data set including 7207 needle examples. A subgroup of the evaluation data set (n = 188) was created, where the needles were digitized once more by a medical physicist (G1) trained in brachytherapy. The digitization procedure was timed. RESULTS: The RMSD between the AI and CGT was 0.55 (IQR: 0.35-0.86) mm. In the smaller subset, the RMSD between AI and CGT was similar (0.52 [IQR: 0.33-0.79] mm) but significantly smaller (P < 0.001) than the difference of 0.75 (IQR: 0.49-1.20) mm between AI and G1. The difference between CGT and G1 was 0.80 (IQR: 0.48-1.18) mm, implying that the AI performed as well as the CGT in relation to G1. The mean time needed for human digitization was 10 min 11 sec, while the time needed for the AI was negligible. CONCLUSIONS: A 3D CNN can be trained to identify needles in TRUS images. The performance of the network was similar to that of a medical physicist trained in brachytherapy. Incorporating a CNN for needle identification can shorten brachytherapy treatment procedures substantially.

Bone Marrow Absorbed Doses and Correlations with Hematologic Response During 177Lu-DOTATATE Treatments Are Influenced by Image-Based Dosimetry Method and Presence of Skeletal Metastases.

This study aimed to compare different image-based methods for bone marrow dosimetry and study the dose-response relationship during treatment with 177Lu-DOTATATE in patients with and without skeletal metastases. Methods: This study included 46 patients with advanced neuroendocrine tumors treated with at least 2 fractions of 177Lu-DOTATATE at Sahlgrenska University Hospital. High- and low-uptake compartments were automatically outlined in planar images collected at 2, 24, 48, and 168 h after injection. The bone marrow absorbed doses were calculated from the cross doses of the high- and low-uptake compartments and the self-dose, using the time-activity concentration curve for the low-uptake compartment. This time-activity concentration curve was adjusted using a fixed constant of 1.8 for the planar dosimetry method and using the activity concentrations in vertebral bodies in SPECT images at 24 h after injection of 177Lu-DOTATATE in 4 hybrid methods: L4-SPECT used the activity concentration in the L4 vertebra, whereas V-SPECT, L-SPECT, and T-SPECT used the median activity concentration in all visible vertebrae, lumbar vertebrae, and thoracic vertebrae, respectively. Results: Using the planar method, L4-SPECT, V-SPECT, L-SPECT, and T-SPECT, the estimated median bone marrow absorbed doses were 0.19, 0.36, 0.40, 0.39, and 0.46 Gy/7.4 GBq, respectively, with respective ranges of 0.12-0.33, 0.15-1.44, 0.19-1.71, 0.21-1.60, and 0.18-2.12 Gy/7.4 GBq. For all methods, the bone marrow absorbed dose significantly correlated with decreased platelet counts. This correlation increased after treatment fraction 2: the Spearman correlation (rs) were -0.49 for the planar method, -0.61 for L4-SPECT, -0.63 for V-SPECT, -0.63 for L-SPECT, and -0.57 for T-SPECT. A separate analysis revealed an increased correlation for patients without skeletal metastases using the planar method (rs = -0.67). In contrast, hybrid methods had poor correlations for patients without metastases and stronger correlations for patients with skeletal metastases (rs = -0.61 to -0.74). The mean bone marrow absorbed doses were 3%-69% higher for patients with skeletal metastases than for patients without. Conclusion: The estimated bone marrow absorbed doses by image-based techniques and the correlation with platelets are influenced by the choice of measured vertebrae and the presence of skeletal metastases.

Continuous-index hidden Markov modelling of array CGH copy number data.

MOTIVATION: In recent years, a range of techniques for analysis and segmentation of array comparative genomic hybridization (aCGH) data have been proposed. For array designs in which clones are of unequal lengths, are unevenly spaced or overlap, the discrete-index view typically adopted by such methods may be questionable or improved. RESULTS: We describe a continuous-index hidden Markov model for aCGH data as well as a Monte Carlo EM algorithm to estimate its parameters. It is shown that for a dataset from the BT-474 cell line analysed on 32K BAC tiling microarrays, this model yields considerably better model fit in terms of lag-1 residual autocorrelations compared to a discrete-index HMM, and it is also shown how to use the model for e.g. estimation of change points on the base-pair scale and for estimation of conditional state probabilities across the genome. In addition, the model is applied to the Glioblastoma Multiforme data used in the comparative study by Lai et al. (Lai,W.R. et al. (2005) Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics, 21, 3763-3370.) giving result similar to theirs but with certain features highlighted in the continuous-index setting.

Regression analysis and modelling of data acquisition for SELDI-TOF mass spectrometry.

MOTIVATION: Pre-processing of SELDI-TOF mass spectrometry data is currently performed on a largel y ad hoc basis. This makes comparison of results from independent analyses troublesome and does not provide a framework for distinguishing different sources of variation in data. RESULTS: In this article, we consider the task of pooling a large number of single-shot spectra, a task commonly performed automatically by the instrument software. By viewing the underlying statistical problem as one of heteroscedastic linear regression, we provide a framework for introducing robust methods and for dealing with missing data resulting from a limited span of recordable intensity values provided by the instrument. Our framework provides an interpretation of currently used methods as a maximum-likelihood estimator and allows theoretical derivation of its variance. We observe that this variance depends crucially on the total number of ionic species, which can vary considerably between different pooled spectra. This variation in variance can potentially invalidate the results from naive methods of discrimination/classification and we outline appropriate data transformations. Introducing methods from robust statistics did not improve the standard errors of the pooled samples. Imputing missing values however-using the EM algorithm-had a notable effect on the result; for our data, the pooled height of peaks which were frequently truncated increased by up to 30%.

Estimating the total rate of DNA replication using branching processes.

Increasing the knowledge of various cell cycle kinetic parameters, such as the length of the cell cycle and its different phases, is of considerable importance for several purposes including tumor diagnostics and treatment in clinical health care and a deepened understanding of tumor growth mechanisms. Of particular interest as a prognostic factor in different cancer forms is the S phase, during which DNA is replicated. In the present paper, we estimate the DNA replication rate and the S phase length from bromodeoxyuridine-DNA flow cytometry data. The mathematical analysis is based on a branching process model, paired with an assumed gamma distribution for the S phase duration, with which the DNA distribution of S phase cells can be expressed in terms of the DNA replication rate. Flow cytometry data typically contains rather large measurement variations, however, and we employ nonparametric deconvolution to estimate the underlying DNA distribution of S phase cells; an estimate of the DNA replication rate is then provided by this distribution and the mathematical model.

Estimating the distribution of the G(2) phase duration from flow cytometric histograms.

A mathematical model, based on branching processes, is proposed to interpret BrdUrd DNA FCM-derived data. Our main interest is in determining the distribution of the G(2) phase duration. Two different model classes involving different assumptions on the distribution of the G(2) phase duration are considered. Different assumptions of the G(2) phase duration result in very similar distributions of the S phase duration and the estimated means and standard deviations of the G(2) phase duration are all in the same range.

Estimating the variation in S phase duration from flow cytometric histograms.

A stochastic model for interpreting BrdUrd DNA FCM-derived data is proposed. The model is based on branching processes and describes the progression of the DNA distribution of BrdUrd-labelled cells through the cell cycle. With the main focus on estimating the S phase duration and its variation, the DNA replication rate is modelled by a piecewise linear function, while assuming a gamma distribution for the S phase duration. Estimation of model parameters was carried out using maximum likelihood for data from two different cell lines. The results provided quite a good fit to the data, suggesting that stochastic models may be a valuable tool for analysing this kind of data.

Segmentation of Whole-Body Images into Two Compartments in Model for Bone Marrow Dosimetry Increases the Correlation with Hematological Response in 177Lu-DOTATATE Treatments.

BACKGROUND: In 177Lu-DOTATATE treatments, bone marrow (BM) is one of the most important organs at risk. The authors previously developed an image-based two-compartment method for BM dosimetry, showing a significant correlation between absorbed dose to BM and hematological toxicity in 177Lu-DOTATATE treatments. In the present study, they aimed to further evaluate this BM dosimetry method by finding optimal settings for dividing the whole body into two compartments; in terms of minimizing the coefficient of variation (CV) for the individual absorbed doses and studying its correlation to the BM response. The authors have also added specific absorbed fractions for male and female. Finally, they compare this two-compartment method with whole-body dosimetry. METHODS: This study included 46 patients with advanced neuroendocrine tumors treated with 177Lu-DOTATATE on two to five occasions at Sahlgrenska University Hospital. Planar gamma camera images were collected at four time points postinjection, and a segmentation tool using a normalized number of uptake foci (nNUF) to divide the whole body into high- and low-uptake compartments was used. The authors characterized the two-compartment model and compared it with whole-body dosimetry. RESULTS AND CONCLUSION: The dosimetry method was robust, with an optimal nNUF value of 0.1-0.2. Using an nNUF value of 0.15, the absorbed BM dose was estimated as 0.20 Gy/7.4 GBq, and the CV as 8.4%. Compared to whole-body dosimetry, stronger correlation was found between absorbed dose to BM and hematological response using the two-compartment method. The two-compartment method has potential as a valuable image-based alternative to blood-based BM dosimetry.

A novel planar image-based method for bone marrow dosimetry in (177)Lu-DOTATATE treatment correlates with haematological toxicity.

BACKGROUND: (177)Lu-DOTATATE is a valuable treatment option for patients with advanced neuroendocrine tumours overexpressing somatostatin receptors. Though well tolerated in general, bone marrow toxicity can, besides renal exposure, become dose limiting and affect the ability to sustain future therapies. The aim of this study was to develop a novel planar image-based method for bone marrow dosimetry and evaluate its correlation with haematological toxicity during (177)Lu-DOTATATE treatment. In this study, 46 patients with advanced neuroendocrine tumours were treated with 7.2 GBq (3.5-8.3 GBq) of (177)Lu-DOTATATE on two to five occasions. Planar gamma camera images were acquired at 2, 24, 48 and 168 h post-injection. Whole-body regions of interest were created in the images, and a threshold-based segmentation algorithm was applied to separate the uptake of (177)Lu-DOTATATE into high and low uptake compartments. The conjugate view method was used to quantify the activity, the accumulated activity was calculated and the absorbed dose to the bone marrow was estimated according to the MIRD scheme. Patients were monitored for haematological toxicity based on haemoglobin (Hb), white blood cell (WBC) and platelet (PLT) counts every other week during the treatment period. RESULTS: The mean absorbed dose to the bone marrow was estimated to 0.20 Gy (0.11-0.37 Gy) per 7.4 GBq of (177)Lu-DOTATATE, and the mean dose per fraction correlated with a decrease in Hb (p = 0.01), WBC (p < 0.01) and PLT (p < 0.01) counts. The total mean absorbed dose to the bone marrow was 0.64 Gy (0.30-1.5 Gy) per 24 GBq (8.2-37 GBq) of (177)Lu-DOTATATE and also correlated with a decrease in Hb (p < 0.01), WBC (p = 0.01) and PLT (p < 0.01) counts. CONCLUSIONS: The planar image-based method developed in this study resulted in similar absorbed doses to the bone marrow as reported in earlier studies with blood-based bone marrow dosimetry. The results correlated with haematological toxicity, making it a promising method for estimating bone marrow doses in (177)Lu-DOTATATE treatment without the need for blood and urine sampling.