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Article ; Online: Hypothyroidism in Cancer Patients on Immune Checkpoint Inhibitors with anti-PD1 Agents: Insights on Underlying Mechanisms.

Alhusseini, M / Samantray, J

Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association

2017 Volume 125, Issue 4, Page(s) 267–269

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2017-04
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1225416-2
ISSN	1439-3646 ; 0947-7349
ISSN (online)	1439-3646
ISSN	0947-7349
DOI	10.1055/s-0042-119528
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Article ; Online: Autoimmune diabetes superimposed on type 2 diabetes in a patient initiated on immunotherapy for lung cancer.

Alhusseini, M / Samantray, J

Diabetes & metabolism

2017 Volume 43, Issue 1, Page(s) 86–88

MeSH term(s)	Aged ; Diabetes Mellitus, Type 1 ; Diabetes Mellitus, Type 2 ; Humans ; Immunotherapy ; Lung Neoplasms/complications ; Lung Neoplasms/therapy ; Male
Language	English
Publishing date	2017-02
Publishing country	France
Document type	Case Reports ; Letter
ZDB-ID	1315751-6
ISSN	1878-1780 ; 1262-3636 ; 0338-1684
ISSN (online)	1878-1780
ISSN	1262-3636 ; 0338-1684
DOI	10.1016/j.diabet.2016.05.007
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Article: Hypothyroidism in Cancer Patients on Immune Checkpoint Inhibitors with anti-PD1 Agents: Insights on Underlying Mechanisms

Alhusseini, M. / Samantray, J.

Experimental and Clinical Endocrinology & Diabetes

2017 Volume 125, Issue 04, Page(s) 267–269

Abstract: Background: Immune therapy using monoclonal antibodies against cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death 1 receptor (PD-1) for various cancers have been reported to cause thyroid dysfunction. Little is known, however, ...

Abstract	Background: Immune therapy using monoclonal antibodies against cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death 1 receptor (PD-1) for various cancers have been reported to cause thyroid dysfunction. Little is known, however, about the underlying pathogenic mechanisms and the course of hypothyroidism that subsequently develops. In this report, we use the change in thyroglobulin and thyroid antibody levels in patients on immune therapy who develop hypothyroidism to better understand its pathogenesis as well as examine the status of hypothyroidism in the long term. Methods: We report a case series of 10 patients who developed hypothyroidism after initiation of immune therapy (either anti-PD-1 alone or in combination with anti-CTLA-4). Available thyroid antibodies including anti-thyroglobulin (anti-Tg), anti-thyroid peroxidase (anti-TPO), and thyroid stimulating immunoglobulin (TSI) were noted during the initial thyroiditis phase as well as the hypothyroid phase. Persistence or remission of hypothyroidism was noted at 6 months. Summary: During the thyroiditis phase, 50% of the patients had elevated Tg titers, 40% had elevated anti-Tg, and 40% had elevated TSI. All of these titers decreased during the hypothyroid phase. Permanent hypothyroidism was noted in 80% of the cases. Conclusion: Hypothyroidism following initiation of immune therapy has immunologic and non-immunologic mediated mechanisms and is likely to be persistent.
Keywords	hypothyroidism ; immunotherapy ; oncology ; thyroiditis ; immune phenomena
Language	English
Publishing date	2017-01-10
Publisher	© Georg Thieme Verlag KG
Publishing place	Stuttgart ; New York
Document type	Article
ZDB-ID	1225416-2
ISSN	1439-3646 ; 0947-7349
ISSN (online)	1439-3646
ISSN	0947-7349
DOI	10.1055/s-0042-119528
Database	Thieme publisher's database

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Article: Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images.

Xu, Isaac R L / Van Booven, Derek J / Goberdhan, Sankalp / Breto, Adrian / Porto, Joao / Alhusseini, Mohammad / Algohary, Ahmad / Stoyanova, Radka / Punnen, Sanoj / Mahne, Anton / Arora, Himanshu

Journal of personalized medicine

2023 Volume 13, Issue 3

Abstract: The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is ... ...

Abstract	The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches.
Language	English
Publishing date	2023-03-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662248-8
ISSN	2075-4426
ISSN	2075-4426
DOI	10.3390/jpm13030547
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Article ; Online: Premarital mental health screening among the Saudi population

Noara Alhusseini, MBBS / Hania Farhan, MBBS / Laiba Yaseen, MBBS / Sara Abid, MBBS / Syeda S. Imad, MBBS / Majed Ramadan

Journal of Taibah University Medical Sciences, Vol 18, Iss 1, Pp 154-

2023 Volume 161

Abstract: الملخص: أهداف البحث: تركز هذه الدراسة على استكشاف الموقف والمعرفة والقبول لفحص الصحة العقلية قبل الزواج. طرق البحث: تم إجراء دراسة مقطعية من خلال استطلاع عبر الإنترنت تم توزيعه باستخدام وسائل التواصل الاجتماعي. صُمم الاستطلاع بمجموعة من 14 سؤالا يجيب ... ...

Abstract	الملخص: أهداف البحث: تركز هذه الدراسة على استكشاف الموقف والمعرفة والقبول لفحص الصحة العقلية قبل الزواج. طرق البحث: تم إجراء دراسة مقطعية من خلال استطلاع عبر الإنترنت تم توزيعه باستخدام وسائل التواصل الاجتماعي. صُمم الاستطلاع بمجموعة من 14 سؤالا يجيب عليها الأفراد الذين تزيد أعمارهم عن 18 عاما والذين يعيشون في المملكة العربية السعودية. كانت استراتيجية أخذ العينات هي العينات المريحة، واستخدمت اختبارات مربع كاي في التحليل الإحصائي. النتائج: تم تلقي 955 إجابة، وكان غالبية المشاركين ليس لديهم تاريخ من الأمراض العقلية. ومع ذلك، كان معظم الأفراد يؤيدون فكرة فحص الصحة العقلية قبل الزواج، لأنهم لا يريدون أن يرث أطفالهم الأمراض الوراثية أو مشاكل الصحة العقلية. ساهم المستوى التعليمي الأعلى للوالدين بشكل كبير في قبول فحص ما قبل الزواج من قبل المستجيبين. أخيرا، كان غالبية المشاركين على دراية بمفهوم متلازمة الفحص قبل الزواج، ولكن لم يكونوا على دراية بإمكانية فحص اضطرابات الصحة العقلية لدى الأفراد الراغبين في الزواج. الخلاصة: سلطت هذه الدراسة الضوء على الموقف الإيجابي والمتقبل لدى السكان السعوديين تجاه فحوصات الصحة العقلية قبل الزواج. ومع ذلك، على الرغم من الاستجابة الإيجابية العامة، لا يزال يُنظر إلى الفحص قبل الزواج على أنه انتهاك لخصوصية الفرد. الزيجات المرتبة في المجتمع ووصمة الصحية النفسية يمكن أن تخلق التردد في اعتماد تدابير الفحص. يمكن استخدام النتائج لزيادة محو الأمية الصحية من خلال توفير معلومات دقيقة ويسهل الوصول إليها، لا سيما لدى كبار السن من السكان. نشجع المتخصصين في الرعاية الصحية ومسؤولي الصحة العامة وواضعي السياسات على زيادة الوعي بفحص الصحة العقلية قبل الزواج وتقديم المشورة حول استشارات الفحص قبل الزواج. Abstract: Objective: This study was aimed at exploring attitudes and knowledge regarding, and acceptance of, premarital mental health screening among the Saudi population. Method: A cross-sectional study was performed via an online survey distributed through social media. The survey comprised a set of 14 questions to be answered by individuals over 18 years of age living in KSA. A convenience sampling strategy was followed, and chi-square tests were used to establish associations. A P-value of 0.05 was ...
Keywords	Consanguineous marriages ; Counselling ; KSA ; Mental health ; Premarital screening ; Medicine (General) ; R5-920
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique.

Algohary, Ahmad / Zacharaki, Evangelia I / Breto, Adrian L / Alhusseini, Mohammad / Wallaengen, Veronica / Xu, Isaac R / Gaston, Sandra M / Punnen, Sanoj / Castillo, Patricia / Pattany, Pradip M / Kryvenko, Oleksandr N / Spieler, Benjamin / Abramowitz, Matthew C / Pra, Alan Dal / Ford, John C / Pollack, Alan / Stoyanova, Radka

NMR in biomedicine

2023 Volume 37, Issue 3, Page(s) e5069

Abstract: Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an ... ...

Abstract	Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (r
MeSH term(s)	Male ; Humans ; Diffusion Magnetic Resonance Imaging/methods ; Prostatic Neoplasms/diagnostic imaging ; Prostatic Neoplasms/pathology ; Magnetic Resonance Imaging/methods ; Biopsy
Language	English
Publishing date	2023-11-21
Publishing country	England
Document type	Journal Article
ZDB-ID	1000976-0
ISSN	1099-1492 ; 0952-3480
ISSN (online)	1099-1492
ISSN	0952-3480
DOI	10.1002/nbm.5069
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Article: Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.

Stoyanova, Radka / Zavala-Romero, Olmo / Kwon, Deukwoo / Breto, Adrian L / Xu, Isaac R / Algohary, Ahmad / Alhusseini, Mohammad / Gaston, Sandra M / Castillo, Patricia / Kryvenko, Oleksandr N / Davicioni, Elai / Nahar, Bruno / Spieler, Benjamin / Abramowitz, Matthew C / Dal Pra, Alan / Parekh, Dipen J / Punnen, Sanoj / Pollack, Alan

Cancers

2023 Volume 15, Issue 21

Abstract: The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification ... ...

Abstract	The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.
Language	English
Publishing date	2023-10-31
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers15215240
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Article: Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy.

Algohary, Ahmad / Alhusseini, Mohammad / Breto, Adrian L / Kwon, Deukwoo / Xu, Isaac R / Gaston, Sandra M / Castillo, Patricia / Punnen, Sanoj / Spieler, Benjamin / Abramowitz, Matthew C / Dal Pra, Alan / Kryvenko, Oleksandr N / Pollack, Alan / Stoyanova, Radka

Cancers

2022 Volume 14, Issue 18

Abstract: We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy ( ... ...

Abstract	We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.
Language	English
Publishing date	2022-09-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14184475
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Article: Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer.

Breto, Adrian L / Spieler, Benjamin / Zavala-Romero, Olmo / Alhusseini, Mohammad / Patel, Nirav V / Asher, David A / Xu, Isaac R / Baikovitz, Jacqueline B / Mellon, Eric A / Ford, John C / Stoyanova, Radka / Portelance, Lorraine

Frontiers in oncology

2022 Volume 12, Page(s) 854349

Abstract: Background/hypothesis: MRI-guided online adaptive radiotherapy (MRI-g-OART) improves target coverage and organs-at-risk (OARs) sparing in radiation therapy (RT). For patients with locally advanced cervical cancer (LACC) undergoing RT, changes in bladder ...

Abstract	Background/hypothesis: MRI-guided online adaptive radiotherapy (MRI-g-OART) improves target coverage and organs-at-risk (OARs) sparing in radiation therapy (RT). For patients with locally advanced cervical cancer (LACC) undergoing RT, changes in bladder and rectal filling contribute to large inter-fraction target volume motion. We hypothesized that deep learning (DL) convolutional neural networks (CNN) can be trained to accurately segment gross tumor volume (GTV) and OARs both in planning and daily fractions' MRI scans. Materials/methods: We utilized planning and daily treatment fraction setup (RT-Fr) MRIs from LACC patients, treated with stereotactic body RT to a dose of 45-54 Gy in 25 fractions. Nine structures were manually contoured. MASK R-CNN network was trained and tested under three scenarios: Results: MRIs from fifteen LACC patients were analyzed. In the LOO scenario the DSC for Rectum, Femur, and Bladder was >0.8, followed by the GTV, Uterus, Mesorectum and Parametrium (0.6-0.7). The results for Vagina and Sigmoid were suboptimal. The performance of the network was similar for most organs when tested on RT-Fr MRI. Including the planning MRI in the training did not improve the segmentation of the RT-Fr MRI. There was a significant correlation between the average organ volume and the corresponding DSC (r = 0.759, p = 0.018). Conclusion: We have established a robust workflow for training MASK R-CNN to automatically segment GTV and OARs in MRI-g-OART of LACC. Albeit the small number of patients in this pilot project, the network was trained to successfully identify several structures while challenges remain, especially in relatively small organs. With the increase of the LACC cases, the performance of the network will improve. A robust auto-contouring tool would improve workflow efficiency and patient tolerance of the OART process.
Language	English
Publishing date	2022-05-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2022.854349
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Article: Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping.

Rodrigo, Miguel / Waddell, Kian / Magee, Sarah / Rogers, Albert J / Alhusseini, Mahmood / Hernandez-Romero, Ismael / Costoya-Sánchez, Alejandro / Liberos, Alejandro / Narayan, Sanjiv M

Frontiers in physiology

2021 Volume 11, Page(s) 611266

Abstract: Introduction: ...

Abstract	Introduction:
Language	English
Publishing date	2021-01-06
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2020.611266
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