PEGylated Pemetrexed and PolyNIPAM Decorated Gold Nanoparticles: A Biocompatible and Highly Stable CT Contrast Agent for Cancer Imaging Mohammad Mohajeri, Peyman Salehi, Bahareh Heidari, Hasan Rafati, S. Mohsen Asghari, Hossein Behboudi, Pooya Iranpour ACS Applied Bio Materials, 2024 This study describes a multifunctional nanoparticle platform for targeted CT imaging and therapy of cancers. Pemetrexed (conjugated with polyethylene glycol, MW 2000 Da) and polyNIPAM (PEGylated) were designed for targeted delivery to folate receptors and thermally ablated tumors, respectively. These moieties were coated on gold nanoparticles (7 and 30 nm), and the prepared compounds were characterized using 1H NMR, FT-IR, CHNS, DLS, TEM, TGA, and UV-vis. The resulting agents exhibited 2-4 times higher X-ray attenuation compared to Visipaque and demonstrated specific accumulation in tumor tissue (4T1 xenograft model) 90 min after injection in mice. The nanoparticles displayed anticancer activity against 4T1 and MDA-MB-231 breast cancer cells (IC50: 182.87 and 206.18 μg/mL) and good biocompatibility. Importantly, the platform showed excellent stability over a year and at pH 2-12 and temperature range of -78 to 40 °C, and a water-dichloromethane extraction method was optimized for efficient purification, facilitating large-scale production.
Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset Isaac Shiri, Yazdan Salimi, Nasim Sirjani, Behrooz Razeghi, Sara Bagherieh, Masoumeh Pakbin, Zahra Mansouri, Ghasem Hajianfar, Atlas Haddadi Avval, Dariush Askari, Mohammadreza Ghasemian, Saleh Sandoughdaran, Ahmad Sohrabi, Elham Sadati, Somayeh Livani, Pooya Iranpour, Shahriar Kolahi, Bardia Khosravi, Salar Bijari, Sahar Sayfollahi, Mohammad Reza Atashzar, Mohammad Hasanian, Alireza Shahhamzeh, Arash Teimouri, Neda Goharpey, Hesamaddin Shirzad‐Aski, Jalal Karimi, Amir Reza Radmard, Kiara Rezaei‐Kalantari, Mostafa Ghelich Oghli, Mehrdad Oveisi, Alireza Vafaei Sadr, Slava Voloshynovskiy, Habib Zaidi Medical Physics, 2024 BackgroundNotwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi‐institutional cohort of patients with COVID‐19 using a DL‐based model.PurposeThis study aimed to evaluate the performance of deep privacy‐preserving federated learning (DPFL) in predicting COVID‐19 outcomes using chest CT images.MethodsAfter applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold‐out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold‐out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences.ResultsThe centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79–0.85) and (95% CI: 0.77–0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p‐value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501.ConclusionThe performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi‐institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.
Acro metastasis: A rare presentation in a common cancer Ali Ghanei-Shahmirzadi, Nasrin Namdari, Maral Mokhtari, Pooya Iranpour Current Problems in Cancer Case Reports, 2024 Case presentation: In this study, we presented an Iranian female with triple-negative breast cancer that developed acro-metastasis to the hand However, bone metastasis is the most common kind of neoplasm extension that we can witness in the breast cancer, acro-metastasis to the hand is an extremely rare kind of bone metastasis that we can expect.
Differentiation of COVID-19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study Isaac Shiri, Yazdan Salimi, Abdollah Saberi, Masoumeh Pakbin, Ghasem Hajianfar, Atlas Haddadi Avval, Amirhossein Sanaat, Azadeh Akhavanallaf, Shayan Mostafaei, Zahra Mansouri, Dariush Askari, Mohammadreza Ghasemian, Ehsan Sharifipour, Saleh Sandoughdaran, Ahmad Sohrabi, Elham Sadati, Somayeh Livani, Pooya Iranpour, Shahriar Kolahi, Bardia Khosravi, Maziar Khateri, Salar Bijari, Mohammad Reza Atashzar, Sajad P. Shayesteh, Mohammad Reza Babaei, Elnaz Jenabi, Mohammad Hasanian, Alireza Shahhamzeh, Seyed Yaser Foroghi Ghomi, Abolfazl Mozafari, Hesamaddin Shirzad‐Aski, Fatemeh Movaseghi, Rama Bozorgmehr, Neda Goharpey, Hamid Abdollahi, Parham Geramifar, Amir Reza Radmard, Hossein Arabi, Kiara Rezaei‐Kalantari, Mehrdad Oveisi, Arman Rahmim, Habib Zaidi International Journal of Imaging Systems and Technology, 2024 To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests.
Glucosamine-Modified Mesoporous Silica-Coated Magnetic Nanoparticles: A “Raisin-Cake”-like Structure as an Efficient Theranostic Platform for Targeted Methotrexate Delivery Fatemeh Farjadian, Zahra Faghih, Maryam Fakhimi, Pooya Iranpour, Soliman Mohammadi-Samani, Mohammad Doroudian Pharmaceutics, 2023 This study presents the synthesis of glucosamine-modified mesoporous silica-coated magnetic nanoparticles (MNPs) as a therapeutic platform for the delivery of an anticancer drug, methotrexate (MTX). The MNPs were coated with mesoporous silica in a templated sol–gel process to form MNP@MSN, and then chloropropyl groups were added to the structure in a post-modification reaction. Glucosamine was then reacted with the chloro-modified structure, and methotrexate was conjugated to the hydroxyl group of the glucose. The prepared structure was characterized using techniques such as Fourier transform infrared (FT-IR) spectroscopy, elemental analysis (CHN), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), a vibrating sample magnetometer (VSM), and X-ray diffraction (XRD). Good formation of nano-sized MNPs and MNP@MSN was observed via particle size monitoring. The modified glucosamine structure showed a controlled release profile of methotrexate in simulated tumor fluid. In vitro evaluation using the 4T1 breast cancer cell line showed the cytotoxicity, apoptosis, and cell cycle effects of methotrexate. The MTT assay showed comparable toxicity between MTX-loaded nanoparticles and free MTX. The structure could act as a glucose transporter-targeting agent and showed increased uptake in cancer cells. An in vivo breast cancer model was established in BALB/C mice, and the distribution of MTX-conjugated MNP@MSN particles was visualized using MRI. The MTX-conjugated particles showed significant anti-tumor potential together with MRI contrast enhancement.
Ethical challenges of artificial intelligence in the field of radiology: a letter to editor Tehran University Medical Journal, 2023
Hydatid Disease: A Pictorial Review of Uncommon Locations Nastaran Khalili, P. Iranpour, Neda Khalili, S. Haseli Iranian Journal of Medical Sciences, 2023 Hydatid disease is a zoonotic infection caused primarily by the tapeworm parasite, Echinococcus granulosus. It is considered an endemic disease in the Mediterranean region. In about 90% of cases, hydatid cysts are found in the liver and lungs; however, any other organ in the body may be affected, particularly in endemic areas. When encountering cystic lesions in these areas, the physician should always keep hydatid disease as a possible diagnosis in mind. To avoid life-threatening conditions such as anaphylactic shock or pressure effect on vital organs, timely diagnosis, and proper management are critical. When a rare site is involved, hydatid disease should be diagnosed using a combination of serologic assays and imaging modalities such as ultrasonography, computed tomography (CT), and magnetic resonance imaging (MRI). These imaging modalities can also be used to determine the extent of the disease and assess possible complications. Here, we present a pictorial review of typical imaging manifestations of hydatid cysts in unusual sites. Being aware of these imaging features will assist physicians in making an accurate, timely diagnosis and subsequently, providing optimal management.
A case of aqueductal obstruction by web with no sign except a headache Fariba Zarei, Banafsheh Zeinali-Rafsanjani, Pooya Iranpour, Sepideh Sefidbakht Radiology Case Reports, 2022 Aqueductal stenosis can be a silent disease that can present in a patient for years without any signs and symptoms. This silence can occur due to CSF flow dynamics compensation, and it can continue until the increase in CSF production so that the symptoms may appear during adolescence or even later. In this study, we report an aqueduct obstruction by web, who had no symptoms except a headache and was referred for MRI in his early thirty. The patient was referred to find the cause of his episodes of headaches. If he did not follow up on his headache, he might never know about his disorder.
PEGylated pemetrexed and polynipam decorated gold nanoparticles: a biocompatible and highly stable CT contrast agent for cancer imaging M Mohajeri, P Salehi, B Heidari, H Rafati, SM Asghari, H Behboudi, ... ACS Applied Bio Materials 7 (9), 5977-5991 , 2024 2024 Citations: 8
Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset I Shiri, Y Salimi, N Sirjani, B Razeghi, S Bagherieh, M Pakbin, Z Mansouri, ... Medical Physics 51 (7), 4736-4747 , 2024 2024 Citations: 12
Acro metastasis: A rare presentation in a common cancer A Ghanei-Shahmirzadi, N Namdari, M Mokhtari, P Iranpour Current Problems in Cancer: Case Reports 14, 100292 , 2024 2024
A versatile theranostic magnetic polydopamine iron oxide NIR laser-responsive nanosystem containing doxorubicin for chemo-photothermal therapy of melanoma M Dehghankhold, F Ahmadi, N Nezafat, M Abedi, P Iranpour, ... Biomaterials Advances 159, 213797 , 2024 2024 Citations: 17
Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: a large multicentric cohort study I Shiri, Y Salimi, A Saberi, M Pakbin, G Hajianfar, AH Avval, A Sanaat, ... International Journal of Imaging Systems and Technology 34 (2), e23028 , 2024 2024 Citations: 6
Mast cell sarcoma of small intestine, early diagnosis, and good prognosis: an extremely rare case report and review of the literature B Geramizadeh, S Nabavizadeh, A Rezvani, N Shamsolvaezin, ... Gastrointestinal Tumors 10 (1), 1-5 , 2023 2023 Citations: 3
Glucosamine-modified mesoporous silica-coated magnetic nanoparticles: A “Raisin-Cake”-like structure as an efficient theranostic platform for targeted methotrexate delivery F Farjadian, Z Faghih, M Fakhimi, P Iranpour, S Mohammadi-Samani, ... Pharmaceutics 15 (10), 2491 , 2023 2023 Citations: 17
Diagnostic performance and safety of positron emission tomography with 18F-rhPSMA-7.3 in patients with newly diagnosed unfavourable intermediate-to very-high-risk prostate … DS Surasi, M Eiber, T Maurer, MA Preston, BT Helfand, D Josephson, ... European urology 84 (4), 361-370 , 2023 2023 Citations: 98
Hydatid disease: a pictorial review of uncommon locations N Khalili, P Iranpour, N Khalili, S Haseli Iranian journal of medical sciences 48 (2), 118 , 2023 2023 Citations: 49
Ethical challenges of artificial intelligence in the field of radiology: a letter to editor P Pishdad Tehran University Medical Journal 81 (3), 246-247 , 2023 2023 Citations: 2
Theranostic hyaluronan coated EDTA modified magnetic mesoporous silica nanoparticles for targeted delivery of cisplatin K Zarkesh, R Heidari, P Iranpour, N Azarpira, F Ahmadi, ... Journal of Drug Delivery Science and Technology 77, 103903 , 2022 2022 Citations: 31
Incidental diagnosis of a rare endobronchial schwannoma in a 7-year-old girl: A case report MG Jahromi, AS Yakhdani, M Saeedi-Moghadam, P Iranpour Radiology Case Reports 17 (10), 4043-4045 , 2022 2022 Citations: 2
A case of aqueductal obstruction by web with no sign except a headache F Zarei, B Zeinali-Rafsanjani, P Iranpour, S Sefidbakht Radiology Case Reports 17 (10), 3767-3769 , 2022 2022 Citations: 4
Currarino syndrome as an incidental radiologic finding in a patient with acute flank pain: A case report MG Jahromi, S Haseli, P Iranpour, AM Nourizadeh Radiology Case Reports 17 (9), 2936-2939 , 2022 2022
The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019 KB Tran, JJ Lang, K Compton, R Xu, AR Acheson, HJ Henrikson, ... The Lancet 400 (10352), 563-591 , 2022 2022 Citations: 1087
The global burden of cancer attributable to risk factors, 2010–19 KB Tran, JJ Lang, K Compton, R Xu, AR Acheson, HJ Henrikson, ... 2022
EVALUATION OF THE PERFORMANCE OF IODINE LOAD REDUCTION AT LOW KVP CT PROTOCOL R Ravanfar Haghighi, S Chatterjee, H Nasrolahi, P Iranpour, F Zarei, ... Iranian Congress of Radiology 37 (2), 21-21 , 2022 2022
Evaluation of ultra-low-dose chest computed tomography images in detecting lung lesions related to COVID-19: A prospective study F Zarei, R Jalli, S Chatterjee, RR Haghighi, P Iranpour, VV Chatterjee, ... Iranian Journal of Medical Sciences 47 (4), 338 , 2022 2022 Citations: 5
Response to “Do not miss Bickerstaff encephalitis as a complication of SARS-CoV-2 vaccines” F Yarmahmoodi, P Iranpoor Radiology Case Reports 17 (9), 3088 , 2022 2022
COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients I Shiri, Y Salimi, M Pakbin, G Hajianfar, AH Avval, A Sanaat, S Mostafaei, ... Computers in biology and medicine 145, 105467 , 2022 2022 Citations: 81
MOST CITED SCHOLAR PUBLICATIONS
The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019 KB Tran, JJ Lang, K Compton, R Xu, AR Acheson, HJ Henrikson, ... The Lancet 400 (10352), 563-591 , 2022 2022 Citations: 1087
Health system performance in Iran: a systematic analysis for the Global Burden of Disease Study 2019 F Farzadfar, M Naghavi, SG Sepanlou, SS Moghaddam, WJ Dangel, ... The Lancet 399 (10335), 1625-1645 , 2022 2022 Citations: 266
Altered Doppler flow patterns in cirrhosis patients: an overview P Iranpour, C Lall, R Houshyar, M Helmy, A Yang, JI Choi, G Ward, ... Ultrasonography 35 (1), 3-12 , 2016 2016 Citations: 130
Diagnostic performance and safety of positron emission tomography with 18F-rhPSMA-7.3 in patients with newly diagnosed unfavourable intermediate-to very-high-risk prostate … DS Surasi, M Eiber, T Maurer, MA Preston, BT Helfand, D Josephson, ... European urology 84 (4), 361-370 , 2023 2023 Citations: 98
COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients I Shiri, Y Salimi, M Pakbin, G Hajianfar, AH Avval, A Sanaat, S Mostafaei, ... Computers in biology and medicine 145, 105467 , 2022 2022 Citations: 81
Statins in patients with COVID-19: a retrospective cohort study in Iranian COVID-19 patients P Peymani, T Dehesh, F Aligolighasemabadi, M Sadeghdoust, K Kotfis, ... Translational medicine communications 6 (1), 3 , 2021 2021 Citations: 67
Hydatid disease: a pictorial review of uncommon locations N Khalili, P Iranpour, N Khalili, S Haseli Iranian journal of medical sciences 48 (2), 118 , 2023 2023 Citations: 49
Synthesis of highly stable and biocompatible gold nanoparticles for use as a new X-ray contrast agent P Iranpour, M Ajamian, A Safavi, N Iranpoor, A Abbaspour, S Javanmardi Journal of Materials Science: Materials in Medicine 29 (5), 48 , 2018 2018 Citations: 41
Methanol toxicity outbreak: when fear of COVID-19 goes viral S Sefidbakht, M Lotfi, R Jalli, M Moghadami, G Sabetian, P Iranpour Emergency medicine journal 37 (7), 416-416 , 2020 2020 Citations: 35
Theranostic hyaluronan coated EDTA modified magnetic mesoporous silica nanoparticles for targeted delivery of cisplatin K Zarkesh, R Heidari, P Iranpour, N Azarpira, F Ahmadi, ... Journal of Drug Delivery Science and Technology 77, 103903 , 2022 2022 Citations: 31
Diagnosis of COVID-19 using CT image radiomics features: a comprehensive machine learning study involving 26,307 patients I Shiri, Y Salimi, A Saberi, M Pakbin, G Hajianfar, AH Avval, A Sanaat, ... medRxiv, 2021.12. 07.21267367 , 2021 2021 Citations: 29
Acute disseminated encephalomyelitis (ADEM) after SARS-CoV-2 vaccination: a case report F Yazdanpanah, P Iranpour, S Haseli, M Poursadeghfard, ... Radiology case reports 17 (5), 1789-1793 , 2022 2022 Citations: 28
Oral radiology center as a potential source of COVID-19 transmission; points to consider M Saki, S Haseli, P Iranpour Academic Radiology 27 (7), 1047 , 2020 2020 Citations: 27
Methanol poisoning emerging as the result of COVID-19 outbreak; radiologic perspective P Iranpour, H Firoozi, S Haseli Academic radiology 27 (5), 755 , 2020 2020 Citations: 27
A new X-ray contrast agent based on highly stable gum arabic-gold nanoparticles synthesised in deep eutectic solvent S Shahidi, S Iranpour, P Iranpour, AA Alavi, FA Mahyari, M Tohidi, ... Journal of Experimental Nanoscience 10 (12), 911-924 , 2015 2015 Citations: 27
Differentiation of chest CT findings between influenza pneumonia and COVID-19: interobserver agreement between radiologists F Zarei, R Jalli, P Iranpour, S Sefidbakht, S Soltanabadi, M Rezaee, ... Academic Radiology 28 (10), 1331-1338 , 2021 2021 Citations: 23
Evaluation of Chest CT Scan as a Screening and Diagnostic Tool inTrauma Patients with Coronavirus Disease 2019 (COVID-19): ACross-Sectional Study M Abdolrahimzadeh Fard, H., Mahmudi-Azer, S., Sefidbakht, S., Iranpour, P ... Emergency Medicine International , 2021 2021 Citations: 21
Lung ultrasound in COVID-19 pneumonia: prospects and limitations S Haseli, P Iranpour Academic radiology 27 (7), 1044 , 2020 2020 Citations: 21
Comparison of the efficacy of oral fenugreek seeds hydroalcoholic extract versus placebo in nonalcoholic fatty liver disease; a randomized, triple-blind controlled pilot … A Babaei, SA Taghavi, A Mohammadi, MA Mahdiyar, P Iranpour, ... Indian journal of pharmacology 52 (2), 86-93 , 2020 2020 Citations: 19
A versatile theranostic magnetic polydopamine iron oxide NIR laser-responsive nanosystem containing doxorubicin for chemo-photothermal therapy of melanoma M Dehghankhold, F Ahmadi, N Nezafat, M Abedi, P Iranpour, ... Biomaterials Advances 159, 213797 , 2024 2024 Citations: 17