Artificial intelligence applications in prostate cancer

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

Post-Prostatectomy Risk Stratification of Biochemical Recurrence Using Transfer Learning-Based Multi-Modal Artificial Intelligence

PURPOSE/OBJECTIVE(S)

For patients undergoing radical prostatectomy for prostate cancer (PCa), accurate risk stratification is essential to guide post-prostatectomy therapeutic decision making. Recently, there has been success in the use of multi-modal artificial intelligence models for men after prostate biopsy to aid in risk stratification. Herein, we trained and tested a TRansfer learning-based multi-modal Artificial InteLligence model (TRAIL) for biochemical recurrence (BCR) risk stratification following radical prostatectomy.

MATERIALS/METHODS

Patients contained within a prospective PCa registry at a single institution were utilized. Digital pathology slides from the diagnostic biopsies prior to radical prostatectomy for patients with clinically localized PCa were scanned at 20x resolution. Features were extracted for the TRAIL model from pathology slides via two transfer learning steps: (1) InceptionResNetv2 that first determines a heatmap of tumor areas, and (2) A ResNet18 that extracts representative features from the high tumor probability areas. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection from the pathology-extracted features. Finally, TRAIL combines the clinical and pathology-extracted features via a classification ensemble model based on weak tree learners to predict 2- and 5-year BCR defined as two consecutive serum PSA levels ≥0.2 ng/mL. TRAIL training was performed on 250 patients and was then locked and applied to the test set of 125 patients. Accuracy and the area under the curve (AUC) were calculated. Comparison to CAPRA-S and to clinical-only features were assessed.

RESULTS

A total of 818 digital whole pathology biopsy slides from 375 patients treated with subsequent radical prostatectomy were included. Surgical margins were positive in 29% of the patients, and 41% had extra-prostatic extension. The median follow-up was 48 months (Range: 1-132 months). The rates of 2-and 5-year BCR were 11% and 18% respectively. A total of 19 digital pathology-driven features were included in TRAIL. Clinical factors included age, ISUPG, Gleason score, PSA, pathological T and N stages, surgical margin involvement, and the presence of extra-prostatic extension. On the testing set, TRAIL achieved a 2-year BCR AUC of 0.76 and accuracy of 0.87, and was superior to CAPRA-S (AUC = 0.57) and clinical-only features (AUC 0.50, accuracy 0.14). For 5-year BCR, TRAIL achieved an AUC of 0.69 and accuracy of 0.78, and performed better than CAPRA-S (AUC = 0.58), and clinical only features (AUC = 0.50, accuracy = 0.23).

CONCLUSION

Through a combination of deep and ensemble learning, TRAIL incorporates clinical and histopathology features, enabling an improved BCR risk stratification post-prostatectomy when compared to the currently used clinicopathologic models. Future work with larger datasets with metastatic events is warranted to further optimize the model for clinical use.

Cross-Comparison Individual Patient Level Analysis of Three Gene Expression Signatures in Localized Prostate in over 50,000 Men

PURPOSE/OBJECTIVE(S)

Risk stratification guides the management of localized prostate cancer. Multiple commercial gene expression biomarkers have been developed to improve estimates of prognosis, however the 22-gene Decipher genomic classifier (22-GC) is the only test with level 1 evidence supporting its use per NCCN guidelines. It is unknown whether other commercial signatures, Oncotype (GPS) or Prolaris (CCP), are sufficiently correlated to negate the differences in evidence supporting these commercial tests. Herein, we aim to perform a cross-comparison of these signatures in a large cohort of patients diagnosed with localized prostate cancer.

MATERIALS/METHODS

Patients diagnosed with localized prostate cancer who underwent whole transcriptome gene expression microarray analysis on their primary tumor biopsy specimen were included. The 22-GC score was calculated by Veracyte using a commercially locked model. Individual genes in each of the GPS and CCP gene signatures were identified, and the gene weights in each signature were retrained for prediction of metastasis in a multi-institutional cohort of 1,574 men with long-term outcome data. This was performed to improve correlation performance of GPS and CCP given only the 22-GC was trained for prediction of metastasis. For each of the three signatures, both continuous and categorical scores were calculated. Linear regression and spearman correlations were calculated both on univariable and multivariable analyses adjusting for age, grade group, PSA, and T-stage.

RESULTS

A total of 50,881 patients were included (15,379 (30.2%) NCCN low-risk, 14,773 (29.0%) favorable intermediate-risk, 15,544 (30.5%) unfavorable intermediate-risk, and 5,185 (10.2%) high/very high-risk) with a median age of 68 years, and a median PSA of 6.2 ng/mL. On linear regression, the GPS model had poor goodness-of-fit to the 22-GC with an R2 of 0.36, as did the CCP model to the 22-GC with an R2 of 0.32. For CCP, the linear sum of the 31-genes was also tested but had inferior performance (R2 0.28) compared to the reoptimized CCP model. Results were similar on multivariable analysis adjusting for age, PSA, clinical stage and grade group. Spearman correlation between the continuous GPS model scores and the 22-GC was moderate at 0.59, as was the correlation between CCP model and the 22-GC of 0.54. CCP is a measure of proliferation, but in 22-GC high-risk patients, the majority (64.1%) of patients had low-average proliferation and only 35.9% had high proliferation, potentially explaining the lack of strong correlation.

CONCLUSION

There is minimal to moderate correlation between the 22-GC and GPS or CCP gene expression signatures tested. Therefore, these tests should not be viewed as interchangeable, and utilization should be based on the level of evidence supporting each gene expression biomarker.

Development and dissemination of structured hashtags for radiation oncology: Two-Year trends

Purpose

For radiation oncology, social media is a favored communication platform, but it uses non-structured hashtags, which limits communication. In this work, we created a set of structured hashtags with key opinion leaders in radiation oncology, and we report on their use after two years post-deployment.

Materials/Methods

Hashtags were created, voted on, and refined by crowdsourcing 38 international experts, including physicians, physicists, patients, and organizations from North America, Europe, and Australia. The finalized hashtag set was shared with the radiation oncology community in September 2019. The number of tweets for each hashtag was quantified via Symplur through December 2021. For the top five tweeted hashtags, we captured the number of yearly tweets in the pre-deployment and post-deployment periods from 09/01/2019 to 08/31/2021.

Results

The initial 2019 list contained 39 hashtags organized into nine categories. The top five hashtags by total number of tweets were: #Radonc, #PallOnc, #MedPhys, #SurvOnc, and #SuppOnc. Six hashtags had less than 10 total tweets and were eliminated. Post-deployment, there was an increase in the yearly tweets, with the following number of tweets by the second year post-deployment: #RadOnc (98,189 tweets), #MedPhys (15,858 tweets), and #SurvOnc (6,361 tweets). Two popular radiation oncology-related hashtags were added because of increased use: #DEIinRO (1,603 tweets by year 2) and #WomenWhoCurie (7,212 tweets by year 2). Over the two years, hashtags were used mostly by physicians (131,625 tweets, 34.8%).

Conclusion

We created and tracked structured social media hashtags in radiation oncology. These hashtags disseminate information among a diverse oncologic community. To maintain relevance, regular updates are needed.

Abdominopelvic MR to CT registration using a synthetic CT intermediate

Accurate coregistration of computed tomography (CT) and magnetic resonance (MR) imaging can provide clinically relevant and complementary information and can serve to facilitate multiple clinical tasks including surgical and radiation treatment planning, and generating a virtual Positron Emission Tomography (PET)/MR for the sites that do not have a PET/MR system available. Despite the long‐standing interest in multimodality co‐registration, a robust, routine clinical solution remains an unmet need. Part of the challenge may be the use of mutual information (MI) maximization and local phase difference (LPD) as similarity metrics, which have limited robustness, efficiency, and are difficult to optimize. Accordingly, we propose registering MR to CT by mapping the MR to a synthetic CT intermediate (sCT) and further using it in a sCT‐CT deformable image registration (DIR) that minimizes the sum of squared differences. The resultant deformation field of a sCT‐CT DIR is applied to the MRI to register it with the CT. Twenty‐five sets of abdominopelvic imaging data are used for evaluation. The proposed method is compared to standard MI‐ and LPD‐based methods, and the multimodality DIR provided by a state of the art, commercially available FDA‐cleared clinical software package. The results are compared using global similarity metrics, Modified Hausdorff Distance, and Dice Similarity Index on six structures. Further, four physicians visually assessed and scored registered images for their registration accuracy. As evident from both quantitative and qualitative evaluation, the proposed method achieved registration accuracy superior to LPD‐ and MI‐based methods and can refine the results of the commercial package DIR when using its results as a starting point. Supported by these, this manuscript concludes the proposed registration method is more robust, accurate, and efficient than the MI‐ and LPD‐based methods.

Relationship between coronary stenosis severity and high shear thrombosis assessment in vitro

Funding Acknowledgements

Type of funding sources: None.

Among stable outpatients presenting with suspected coronary artery disease, the presence and extent of coronary artery calcification (CAC) and the severity of disease on CT coronary angiography (CTCA) has been shown to be predictive of future major adverse cardiovascular events (MACE) including myocardial infarction (MI). In stable patients, high on-treatment platelet reactivity has also been shown to relate to an increased risk of MACE including MI. The relationship between thrombotic markers in peripheral blood and the extent of CAC and coronary disease severity, is unknown.

It was the aim of this pilot study to assess the relationship between thrombotic status and the extent of CAC and severity of coronary stenosis on CT.

Subjects with suspected coronary disease undergoing CTCA and CAC were invited to participate in this observational study. Venous blood was obtained to assess platelet reactivity to high shear (occlusion time, OT) and endogenous fibrinolysis (lysis time, LT) using the Global Thrombosis Test, and related to CAC and to maximum stenosis in any main coronary artery on CTCA.

Eighty patients were recruited, specifically 20 patients from each CAC quartile (adjusted for age, gender and ethnicity), 58% were male, aged 61±10 y. Groups were matched for age, sex, diabetes, and hs-CRP. The median Agatson CAC score was 27 [interquartile range (IQR) 0.5-125.5] and in each quartile (Q) as follows: Q1 0[0-0]; Q2 17[6-51.5]; Q3 70.25[26-111.5] and Q4 192.6[70.5-413.5].

Patients were divided into 4 groups according to maximal severity of coronary stenosis on CTCA (0%, 1-49%, 50-69%, >70%). With increasing stenosis severity, we found patients exhibited less efficient endogenous fibrinolysis (longer LT) (LT 1728s[1512-2102] vs. 2028s[1687-2288] vs. 1728s[1634-1927] vs. 2524s[2425-2623] respectively, p=0.040) whilst platelet reactivity appeared unrelated to severity of coronary stenosis (438s[341-479] vs. 415s[357-484] vs. 444s[384-504] vs. 391s[357-425], p=0.907).

Platelet reactivity (OT 430s[339-477] vs. 458s[391-499] vs. 409s[351-488] vs. 413s[354-496], p=0.76) and spontaneous fibrinolysis (LT 1754s[1548-2162] vs. 1809s[1635-2291] vs. 2111s[1838-2312] vs. 1846s[1666-2090], p=0.253) were similar between the quartiles. Furthermore, there was no difference in platelet reactivity (430s[339-477] vs. 413s[354-496], p=0.830) or spontaneous fibrinolysis (1754s[1548-2162] vs. 1846s[1666-2090], p=0.561) when comparing patients within the lowest and the highest quartiles of CAC.

The severity of maximal coronary stenosis, but not the extent of CAC, is related to the effectiveness of spontaneous fibrinolysis at high shear in vitro, with patients with more severe stenoses exhibiting less efficient fibrinolysis. Further studies are required to investigate whether the extent of in vivo coronary shear (related to plaque morphology) can be reflected by the assessment of thrombosis and fibrinolysis in response to high shear in vitro.

Outcomes and toxicities in oligometastatic patients treated with stereotactic body radiotherapy for adrenal gland metastases: A multi-institutional retrospective study

•

SBRT to adrenal gland oligometastases achieves a satisfactory local control and OS.

•

A minimum PTV dose BED₁₀ > 46 Gy was associated with an improved OS and LRFS.

•

A prescribed BED₁₀ > 70 Gy was correlated with improved local control.

•

High adrenal metastases volume should not preclude the delivery of SBRT.

Background

Studies reporting SBRT outcomes in oligometastatic patients with adrenal gland metastases (AGM) are limited. Herein, we present a multi-institutional analysis of oligometastatic patients treated with SBRT for AGM.

Material/methods

The Consortium for Oligometastases Research (CORE) is among the largest retrospective series of patients with oligometastases. Among CORE patients, those treated with SBRT for AGM were included. Clinical and dosimetric data were collected. Adrenal metastatic burden (AMB) was defined as the sum of all adrenal GTV if more than one oligometastases is present.

Competing risk analysis was used to estimate actuarial cumulative local recurrence (LR) and widespread progression (WP). Kaplan-Meier method was used to report overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). Treatment related toxicities were also reported.

Results

The analysis included 47 patients with 57 adrenal lesions. Median follow-up was 18.2 months. Median LRFS, PFS, and OS were 15.3, 5.3, and 19.1 months, respectively. A minimum PTV dose BED₁₀ > 46 Gy was associated with an improved OS and LRFS. A prescribed BED₁₀ > 70 Gy was an independent predictor of a lower LR probability. AMB>10 cc was an independent predictor of a lower risk for WP. Only one patient developed an acute Grade 3 toxicity consisting of abdominal pain.

Conclusion

SBRT to AGM achieved a satisfactory local control and OS in oligometastatic patients. High minimum PTV dose and BED₁₀ prescription doses were predictive of improved LR and OS, respectively. Prospective studies are needed to determine comprehensive criteria for patients SBRT eligibility and dosimetric planning.

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of 18F-labeled 2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET), magnetic resonance (MR), and computed tomography (CT) images. Thereafter, images are co-registered to derive electron density attributes required for FDG-PET attenuation correction and radiation therapy planning. Nevertheless, this traditional approach is subject to MR-CT registration defects, expands treatment expenses, and increases the patient's radiation exposure. To overcome these disadvantages, we propose a new framework for cross-modality image synthesis which we apply on MR-CT image translation for cervical cancer diagnosis and treatment. The framework is based on a conditional generative adversarial network (cGAN) and illustrates a novel tactic that addresses, simplistically but efficiently, the paradigm of vanishing gradient vs. feature extraction in deep learning. Its contributions are summarized as follows: 1) The approach -termed sU-cGAN-uses, for the first time, a shallow U-Net (sU-Net) with an encoder/decoder depth of 2 as generator; 2) sU-cGAN's input is the same MR sequence that is used for radiological diagnosis, i.e. T2-weighted, Turbo Spin Echo Single Shot (TSE-SSH) MR images; 3) Despite limited training data and a single input channel approach, sU-cGAN outperforms other state of the art deep learning methods and enables accurate synthetic CT (sCT) generation. In conclusion, the suggested framework should be studied further in the clinical settings. Moreover, the sU-Net model is worth exploring in other computer vision tasks.

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM; 2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers; 3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis. Applying the method on twenty subjects' feature data of UTE-mDixon MR images, the average score of the mean absolute prediction deviation (MAPD) of all subjects is 140.72 ± 30.60 HU which is statistically significantly better than the 241.36 ± 21.79 HU obtained using the all-water method, the 262.77 ± 42.22 HU obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method, and the 197.05 ± 76.53 HU obtained via the conventional SVM method. These results demonstrate the effectiveness of our method for the intelligent transformation from MR to CT on the body section of abdomen-pelvis.

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multi-center trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less. These experimental results distinctively demonstrate the effectiveness of our proposed TFCALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence.

UTE-mDixon-based thorax synthetic CT generation

PURPOSE

Accurate photon attenuation assessment from MR data remains an unmet challenge in the thorax due to tissue heterogeneity and the difficulty of MR lung imaging. As thoracic tissues encompass the whole physiologic range of photon absorption, large errors can occur when using, for example, a uniform, water-equivalent or a soft-tissue-only approximation. The purpose of this study was to introduce a method for voxel-wise thoracic synthetic CT (sCT) generation from MR data attenuation correction (AC) for PET/MR or for MR-only radiation treatment planning (RTP).

METHODS

Acquisition: A radial stack-of-stars combining ultra-short-echo time (UTE) and modified Dixon (mDixon) sequence was optimized for thoracic imaging. The UTE-mDixon pulse sequence collects MR signals at three TE times denoted as UTE, Echo1, and Echo2. Three-point mDixon processing was used to reconstruct water and fat images. Bias field correction was applied in order to avoid artifacts caused by inhomogeneity of the MR magnetic field.

ANALYSIS

Water fraction and R2* maps were estimated using the UTE-mDixon data to produce a total of seven MR features, that is UTE, Echo1, Echo2, Dixon water, Dixon fat, Water fraction, and R2*. A feature selection process was performed to determine the optimal feature combination for the proposed automatic, 6-tissue classification for sCT generation. Fuzzy c-means was used for the automatic classification which was followed by voxel-wise attenuation coefficient assignment as a weighted sum of those of the component tissues. Performance evaluation: MR data collected using the proposed pulse sequence were compared to those using a traditional two-point Dixon approach. Image quality measures, including image resolution and uniformity, were evaluated using an MR ACR phantom. Data collected from 25 normal volunteers were used to evaluate the accuracy of the proposed method compared to the template-based approach. Notably, the template approach is applicable here, that is normal volunteers, but may not be robust enough for patients with pathologies.

RESULTS

The free breathing UTE-mDixon pulse sequence yielded images with quality comparable to those using the traditional breath holding mDixon sequence. Furthermore, by capturing the signal before T2* decay, the UTE-mDixon image provided lung and bone information which the mDixon image did not. The combination of Dixon water, Dixon fat, and the Water fraction was the most robust for tissue clustering and supported the classification of six tissues, that is, air, lung, fat, soft tissue, low-density bone, and dense bone, used to generate the sCT. The thoracic sCT had a mean absolute difference from the template-based (reference) CT of less than 50 HU and which was better agreement with the reference CT than the results produced using the traditional Dixon-based data.

CONCLUSION

MR thoracic acquisition and analyses have been established to automatically provide six distinguishable tissue types to generate sCT for MR-based AC of PET/MR and for MR-only RTP.

Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach

BACKGROUND

Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring.

METHODS/MATERIALS

Our algorithm is based on two types of information flow, i.e. top-down and bottom-up. Top-down information is derived from simulation MR images. It grossly delineates the object based on its high-level information class by transferring the initial planning contours onto daily images. Bottom-up information is derived from pixel data by a supervised, self-adaptive, active learning based support vector machine. It uses low-level pixel features, such as intensity and location, to distinguish each target boundary from the background. The final result is obtained by fusing top-down and bottom-up outputs in a unified framework through artificial intelligence fusion. For evaluation, we used a dataset of four patients with locally advanced pancreatic cancer treated with MR-ART using a clinical system (MRIdian, Viewray, Oakwood Village, OH, USA). Each set included the simulation MRI and onboard T1 MRI corresponding to a randomly selected treatment session. Each MRI had 144 axial slices of 266 × 266 pixels. Using the Dice Similarity Index (DSI) and the Hausdorff Distance Index (HDI), we compared the manual and automated contours for the liver, left and right kidneys, and the spinal cord.

RESULTS

The average auto-segmentation time was two minutes per set. Visually, the automatic and manual contours were similar. Fused results achieved better accuracy than either the bottom-up or top-down method alone. The DSI values were above 0.86. The spinal canal contours yielded a low HDI value.

CONCLUSION

With a DSI significantly higher than the usually reported 0.7, our novel algorithm yields a high segmentation accuracy. To our knowledge, this is the first fully automated contouring approach using T1 MRI images for adaptive radiotherapy.

Comparative analysis for renal stereotactic body radiotherapy using Cyberknife, VMAT and proton therapy based treatment planning

PURPOSE

We conducted this dosimetric analysis to evaluate the feasibility of a multi-center stereotactic body radiation therapy (SBRT) trial for renal cell carcinoma (RCC) using different SBRT platforms.

MATERIALS/METHODS

The computed tomography (CT) simulation images of 10 patients with unilateral RCC previously treated on a Phase 1 trial at Institution 1 were anonymized and shared with Institution 2 after IRB approval. Treatment planning was generated through five different platforms aiming a total dose of 48 Gy in three fractions. These platforms included: Cyberknife and volumetric modulated arc therapy (VMAT) at institution 1, and Cyberknife, VMAT, and pencil beam scanning (PBS) Proton Therapy at institution 2. Dose constraints were based on the Phase 1 approved trial.

RESULTS

Compared to Cyberknife, VMAT and PBS plans provided overall an equivalent or superior coverage to the target volume, while limiting dose to the remaining kidney, contralateral kidney, liver, spinal cord, and bowel.

CONCLUSION

This dosimetric study supports the feasibility of a multi-center trial for renal SBRT using PBS, VMAT and Cyberknife.

Three-dimensional (3D) culture of adult murine colon as an in vitro model of cryptosporidiosis: Proof of concept

Cryptosporidium parvum is a major cause of diarrheal illness and was recently potentially associated with digestive carcinogenesis. Despite its impact on human health, Cryptosporidium pathogenesis remains poorly known, mainly due to the lack of a long-term culture method for this parasite. Thus, the aim of the present study was to develop a three-dimensional (3D) culture model from adult murine colon allowing biological investigations of the host-parasite interactions in an in vivo-like environment and, in particular, the development of parasite-induced neoplasia. Colonic explants were cultured and preserved ex vivo for 35 days and co-culturing was performed with C. parvum. Strikingly, the resulting system allowed the reproduction of neoplastic lesions in vitro at 27 days post-infection (PI), providing new evidence of the role of the parasite in the induction of carcinogenesis. This promising model could facilitate the study of host-pathogen interactions and the investigation of the process involved in Cryptosporidium-induced cell transformation.

Outcomes and toxicities in patients treated with definitive focal therapy for primary prostate cancer: systematic review

AIM

This systematic review summarizes the clinical data on focal therapy (FT) when used alone as definitive therapy for primary prostate cancer (PCa).

METHODS

The protocol is detailed in the online PROSPERO database, registration No. CRD42014014765. Articles evaluating any form of FT alone as a definitive treatment for PCa in adult male patients were included.

RESULTS

Of 10,419 identified articles, 10,401 were excluded, and thus leaving 18 for analysis. In total, 2288 patients were treated using seven modalities. The outcomes of FT in PCa seem to be similar to those observed with whole gland therapy and with fewer side effects.

CONCLUSION

Further research, including prospective randomized trials, is warranted to elucidate the potential advantages of focal radiation techniques for treating PCa. Prospero Registration Number: CRD42014014765.

Saphenous vein graft aneurysm presenting as abdominal pain

Aneurysmal dilation of a saphenous vein aortocoronary graft remains a rare complication. We report a patient with saphenous vein graft aneurysm who presented with abdominal pain due to compression of the adjacent liver 43 years after the coronary bypass operation.

Hematological manifestations of celiac disease

Celiac disease, an autoimmune disease once thought to be uncommon, is now being increasingly identified. Our improved diagnostic modalities have allowed us to diagnose more and more patients with atypical symptoms who improve on gluten-free diet (GFD). We discuss here the latest findings regarding the various hematological manifestations of celiac disease and their management. Anemia remains the most common hematological manifestation of celiac disease due to many mechanisms, and can be the sole presenting symptom. Other manifestations include thrombocytosis and thrombocythemia, leukopenia, thromboembolism, increased bleeding tendency, IgA deficiency, splenic dysfunction, and lymphoma. The diagnosis of celiac disease should always be kept in mind when a patient presents with unexplained and isolated hematological finding. Once diagnosed, patients should adhere to GFD and be educated about the potential complications of this disease. We herein present an algorithm for adequate management and follow-up.

Physical symptom profile for adult cancer inpatients at a Lebanese cancer unit

BACKGROUND

Hospital-based palliative care programs in Lebanon are nonexistent in a structured form. One of obstacles is the lack of knowledge about symptom prevalence and burden of cancer patients in Lebanon.

METHODS

This is a cross-sectional observational study where 100 adult cancer patients admitted to the American University of Beirut Medical Center inpatient unit completed a survey to assess 20 physical symptoms according the National Cancer Institute's Common Terminology Criteria for Adverse Events 4.0 (NCI-CTCAE 4.0) guidelines.

RESULTS

Hematologic, gastrointestinal, breast, and lung cancers were the most common. Mean age was 51.5 years; 51% were female. 74% of patients with solid tumors had metastatic disease. Treatment approaches were palliative chemotherapy, followed by curative chemotherapy and best supportive care. The most common symptoms were fatigue, appetite loss, nausea, and pain; most distressing were nausea, pain, and fatigue. Nausea and vomiting were more prevalent among females than males. Females reported more severe vomiting than males, but males had more intense pain. Overall symptom burden difference was statistically significant across age groups, with the 51-60 year group having the most symptoms, but not among different genders. Difference was significant among different treatment intents, with the best supportive care group having most symptoms.

CONCLUSION

Fatigue should be better addressed as a legitimate symptom. Subgroup differences must be considered when managing gastrointestinal symptoms. Pain should be more effectively managed, and vulnerable subgroups such as the 51-60 year age group and those on best supportive care should receive special consideration.

Signal transduction of cannabinoid CB1 receptors in a smooth muscle cell line

1. The effects of cannabinoid (CB) receptor stimulation on membrane currents in single cells from the Syrian hamster vas deferens cell line DDT1MF-2 were investigated using the whole cell patch clamp technique. 2. The CB receptor agonist CP55,940 evoked a concentration-dependent transient outward current. The selective CB1 receptor ligand SR141716 (1 microM), but not the selective CB2 receptor ligand SR144528 (1 microM), inhibited the outward current. Pertussis toxin (100 ng ml-1 for 20 h) completely abolished the outward current. 3. Western blotting with an antibody against the rat (r)CB1 receptor showed a band characteristic for the CB1 receptor around 63 kDa in DDT1MF-2 cells. 4. The reversal potential for the outward current measured using a voltage ramp protocol was -84 +/- 5 mV. The current was inhibited by the Ca2+-dependent K+ channel blockers iberiotoxin (10 nM) and charybdotoxin (10 nM). 5. Removal of Ca2+ from the bathing solution, or the addition of 0.1 mM Cd2+ completely abolished the outward current evoked by 10 microM CP55,940. 6. The sarcoplasmic Ca2+ pump inhibitor thapsigargin reduced the outward current evoked by 10 microM CP55,940 in a concentration-dependent manner. 7. The mitogen-activating protein kinase (MAP kinase) inhibitor PD98059, but not the phospholipase C inhibitor U73122, inhibited the outward current evoked by 10 microM CP55,940. 8. The adenylyl cyclase inhibitor SQ22,536 (100 microM) and 8-Br-cyclic AMP (10 microM) significantly reduced the outward current evoked by 10 microM CP55,940. 9. Our data suggest that CB1 receptor stimulation in DDT1MF-2 cells leads to activation of a large conductance Ca2+-dependent K+ channel through a Gi/Go protein-mediated rise in [Ca2+]i, for which both inhibition of adenylyl cyclase and activation of MAP kinase are required. In addition, the cannabinoid-induced increase in [Ca2+]i is likely to arise from capacitive Ca2+ entry.

In vitro and in vivo TNFalpha synthesis modulation by methylguanidine, an uremic catabolyte

This study was performed in order to examine whether the uraemic toxin, methylguanidine (MG), can modulate tumor necrosis factor alpha (TNF alpha) release by activated macrophages. In this study we have evaluated the ability of MG to influence TNF alpha release in vitro, in Escherichia coli lypopolysaccharide- (LPS)-stimulated J774 cells preincubated overnight with MG, and in vivo in rats treated with MG before and after LPS challenge. Parallel experiments employing N(G)-nitro-L-arginine methyl esther (L-NAME) were also carried out for comparison. The effect of LPS (6 x 10(3) u/ml) on TNF alpha release by J774, following overnight incubation with MG or L-NAME (1 mM), was examined 3 hours after LPS challenge. LPS-stimulated J774 released 287.83+/-88 u/ml TNF alpha into the culture medium. MG (1 mM) significantly inhibited TNF alpha release by 73% (P<0.05). L-NAME (1 mM) significantly inhibited TNF alpha release too by 72.88% (P<0.05). The effect of MG and L-NAME have been also studied in vivo. Serum TNF alpha levels in LPS treated rats 2 h after LPS challenge were 88.33+/-31.7 u/ml as compared to the serum TNF alpha levels of control rats (undetectable). Treatment of rats with MG (30 mg/kg, i.p.) strongly and significantly reduced TNF alpha release (98.71% inhibition; with P<0.001); in the same experimental setting L-NAME (10 mg/kg, i.p.) also significantly reduced TNF alpha serum levels (76.47% inhibition; with P<0.01). These results could indicate that immune disfunction related to uremia may be related to the inhibitory capability of uremic catabolyte, MG, on TNF alpha synthesis and release.