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2. Using Machine-Learned Bayesian Belief Networks to Predict Perioperative Risk of Clostridium Difficile Infection Following Colon Surgery

Author

Steele, Scott, Bilchik, Anton, Eberhardt, John, Kalina, Philip, Nissan, Aviram, Johnson, Eric, Avital, Itzhak, and Stojadinovic, Alexander

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5
Abstract

BackgroundClostridium difficile (C-Diff) infection following colorectal resection is an increasing source of morbidity and mortality. ObjectiveWe sought to determine if machine-learned Bayesian belief networks (ml-BBNs) could preoperatively provide clinicians with postoperative estimates of C-Diff risk. MethodsWe performed a retrospective modeling of the Nationwide Inpatient Sample (NIS) national registry dataset with independent set validation. The NIS registries for 2005 and 2006 were used for initial model training, and the data from 2007 were used for testing and validation. International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes were used to identify subjects undergoing colon resection and postoperative C-Diff development. The ml-BBNs were trained using a stepwise process. Receiver operating characteristic (ROC) curve analysis was conducted and area under the curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) were calculated. ResultsFrom over 24 million admissions, 170,363 undergoing colon resection met the inclusion criteria. Overall, 1.7% developed postoperative C-Diff. Using the ml-BBN to estimate C-Diff risk, model AUC is 0.75. Using only known a priori features, AUC is 0.74. The model has two configurations: a high sensitivity and a high specificity configuration. Sensitivity, specificity, PPV, and NPV are 81.0%, 50.1%, 2.6%, and 99.4% for high sensitivity and 55.4%, 81.3%, 3.5%, and 99.1% for high specificity. C-Diff has 4 first-degree associates that influence the probability of C-Diff development: weight loss, tumor metastases, inflammation/infections, and disease severity. ConclusionsMachine-learned BBNs can produce robust estimates of postoperative C-Diff infection, allowing clinicians to identify high-risk patients and potentially implement measures to reduce its incidence or morbidity.

Published
2012
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6. Prognostic Effect of Ultra-Staging Node-Negative Colon Cancer Without Adjuvant Chemotherapy: A Prospective National Cancer Institute-Sponsored Clinical Trial

Author

Protic, Mladjan, Stojadinovic, Alexander, Nissan, Aviram, Wainberg, Zev, Steele, Scott R, Chen, David C, Avital, Itzhak, and Bilchik, Anton J

Subjects
Colo-Rectal Cancer, Digestive Diseases, Clinical Research, Clinical Trials and Supportive Activities, Cancer, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Adenocarcinoma, Aged, Aged, 80 and over, Chemotherapy, Adjuvant, Colectomy, Colonic Neoplasms, Disease-Free Survival, Female, Humans, Lymph Nodes, Male, Middle Aged, Neoplasm Staging, Prognosis, Prospective Studies, Clinical Sciences, Surgery
Abstract

BackgroundWe recently reported, in a prospective randomized trial, that ultra-staging of patients with colon cancer is associated with significantly improved disease-free survival (DFS) compared with conventional staging. That trial did not control for lymph node (LN) number or adjuvant chemotherapy use.Study designThe current international prospective multicenter cooperative group trial (ClinicalTrials.gov identifier NCT00949312; "Ultra-staging in Early Colon Cancer") evaluates the 12-LN quality measure and nodal ultra-staging impact on DFS in patients not receiving adjuvant chemotherapy. Eligibility criteria included biopsy-proven colon adenocarcinoma; absence of metastatic disease; >12 LNs staged pathologically; pan-cytokeratin immunohistochemistry (IHC) of hematoxylin and eosin (H&E)-negative LNs; and no adjuvant chemotherapy.ResultsOf 445 patients screened, 203 patients were eligible. The majority of patients had intermediate grade (57.7%) and T3 tumors (64.9%). At a mean follow-up of 36.8 ± 22.1 months (range 0 to 97 months), 94.3% remain disease free. Recurrence was least likely in patients with ≥12 LNs, H&E-negative LNs, and IHC-negative LNs (pN0i-): 2.6% vs 16.7% in the pN0i+ group (p < 0.0001).ConclusionsThis is the first prospective report to demonstrate that patients with optimally staged node-negative colon cancer (≥12 LNs, pN0i-) are unlikely to benefit from adjuvant chemotherapy; 97% remain disease free after primary tumor resection. Both surgical and pathologic quality measures are imperative in planning clinical trials in nonmetastatic colon cancer.

Published
2015

7. Duodenal ischemia and upper GI bleeding are dose-limiting toxicities of 24-h continuous intra-arterial pancreatic perfusion of gemcitabine following vascular isolation of the pancreatic head: early results from the Regional Chemotherapy in Locally Advanced Pancreatic Cancer (RECLAP) study

Author

Beane, Joal D, Griffin, Kayla F, Levy, Elliot B, Pandalai, Prakash, Wood, Bradford, Abi-Jaoudeh, Nadine, Beresnev, Tatiana, Shutack, Yvonne, Webb, Carole C, Avital, Itzhak, and Rudloff, Udo

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Clinical Trials and Supportive Activities, Orphan Drug, Clinical Research, Digestive Diseases, Pancreatic Cancer, Rare Diseases, Patient Safety, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Aged, Antimetabolites, Antineoplastic, Carcinoma, Pancreatic Ductal, Catheterization, Deoxycytidine, Duodenal Diseases, Duodenum, Female, Gastrointestinal Hemorrhage, Humans, Ischemia, Male, Maximum Tolerated Dose, Middle Aged, Pancreas, Pancreatic Neoplasms, Perfusion, Splenic Artery, Treatment Outcome, Gemcitabine, Pancreatic cancer, Fixed dose rate, Regional intra-arterial chemotherapy, Regional pancreatic perfusion, Duodenal ischemia, Pharmacology and Pharmaceutical Sciences, Oncology & Carcinogenesis, Oncology and carcinogenesis, Pharmacology and pharmaceutical sciences
Abstract

BackgroundRegional chemotherapy is used successfully in the treatment of both primary and secondary malignancies, in particular of the peritoneal surface and the liver, and is currently explored as an attractive approach for patients with locally advanced pancreatic ductal adenocarcinoma. To establish the feasibility and toxicity of regional intra-arterial gemcitabine delivered as a 24-h continuous infusion to the pancreas as a novel treatment option for patients with locally advanced PDAC a phase I clinical trial was conducted.MethodsBetween April 2011 and September 2013 six patients with biopsy confirmed, borderline or unresectable pancreatic adenocarcinoma, and having received at least one line of systemic chemotherapy, underwent vascular redistribution of the inflow to the head of the pancreas by arterial coil embolization followed by perfusion catheter placement within the splenic artery. Patients were treated with increasing doses of gemcitabine administered by continuous splenic arterial infusion over 24 h with inter-patient and intra-patient dose escalation scheme. The primary endpoint was toxicity of the intra-arterial gemcitabine regimen and to establish the maximum tolerated dose.ResultsCatheter placement and gemcitabine infusion was successful in all patients enrolled to date (n = 6). Four out of six patients experienced catheter tip migration requiring replacement or revision. Patients received a median of four doses of 24-h gemcitabine infusion. Two patients developed grade 3 and 4 duodenal ischemia and upper gastrointestinal bleeding. Median overall survival was 15.3 months and median time to progression was 3 months. Three patients (50 %, n = 3/6) progressed systemically. Two patients had stable disease >4 months following treatment and underwent pancreaticoduodenectomy.ConclusionsWhile technically feasible to treat locally advanced pancreatic ductal adenocarcinoma, prolonged regional pancreatic perfusion with gemcitabine following pancreatic arterial redistribution carries a high risk for gastrointestinal toxicity. Shorter infusion schedules with frequent on treatment evaluations should be considered for future clinical trials.

Published
2015

9. Treatment of Peritoneal Surface Malignancies with Cytoreductive Surgery and Hyperthermic Intra-peritoneal Chemotherapy (HIPEC): Experience in Israel

Author

Ben-Yaacov, Almog, Nizri, Eran, Lahat, Guy, Berger, Yaniv, Sadot, Eran, Lavi, Ron, Zippel, Dov, Sebbag, Gilbert, Avital, Itzhak, Hoffman, Aviad, Gutman, Mordechai, Halevy, Ariel, Schneibaum, Shlomo, Ben-Ari, Gur, Benchimol, Daniel, Klausner, Joseph, Nissan, Aviram, and For the Israeli Peritoneal Oncology Research Group

Published
2019
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10. Supplementary Figures 1-3, Tables 1-3, Methods from Definition of Ubiquitination Modulator COP1 as a Novel Therapeutic Target in Human Hepatocellular Carcinoma

Author

Lee, Yun-Han, primary, Andersen, Jesper B., primary, Song, Ho-Taek, primary, Judge, Adam D., primary, Seo, Daekwan, primary, Ishikawa, Tsuyoshi, primary, Marquardt, Jens U., primary, Kitade, Mitsuteru, primary, Durkin, Marian E., primary, Raggi, Chiara, primary, Woo, Hyun Goo, primary, Conner, Elizabeth A., primary, Avital, Itzhak, primary, MacLachlan, Ian, primary, Factor, Valentina M., primary, and Thorgeirsson, Snorri S., primary

Published
2023
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11. Data from Definition of Ubiquitination Modulator COP1 as a Novel Therapeutic Target in Human Hepatocellular Carcinoma

Author

Lee, Yun-Han, primary, Andersen, Jesper B., primary, Song, Ho-Taek, primary, Judge, Adam D., primary, Seo, Daekwan, primary, Ishikawa, Tsuyoshi, primary, Marquardt, Jens U., primary, Kitade, Mitsuteru, primary, Durkin, Marian E., primary, Raggi, Chiara, primary, Woo, Hyun Goo, primary, Conner, Elizabeth A., primary, Avital, Itzhak, primary, MacLachlan, Ian, primary, Factor, Valentina M., primary, and Thorgeirsson, Snorri S., primary

Published
2023
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15. Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT

Author

Talia Yeshua, Shmuel Ladyzhensky, Amal Abu-Nasser, Ragda Abdalla-Aslan, Tami Boharon, Avital Itzhak-Pur, Asher Alexander, Akhilanand Chaurasia, Adir Cohen, Jacob Sosna, Isaac Leichter, and Chen Nadler

Subjects
Radiology, Nuclear Medicine and imaging, General Medicine
Abstract

Objectives: This study aimed to develop an automated deep-learning algorithm for the detection and 3D segmentation of incidental jaw lesions in maxillofacial CBCT scans.Materials and Methods: The dataset included 82 CBCT scans with and without histologically confirmed benign bone lesions, obtained from three CBCT devices using different imaging protocols. The dataset consisted of axial CBCT images and was divided into training dataset (20,214 axial images), validation dataset (4,530 axial images) and testing dataset (6,795 axial images). A Mask-RCNN based deep-learning algorithm segmented the bone lesion in each axial image. The analysis of sequential slices improved the mask RCNN performance and assisted in classifying each CBCT case as containing bone lesions or not. Thereafter, the algorithm generated a 3D segmentation of the lesions. Results: The accuracy of the algorithm for classifying each CBCT case as either containing bone lesions or not, was 100%. The algorithm performance for detecting lesions in individual axial images showed high sensitivity (95.9%) and high precision (98.9%).Conclusions: Our deep learning algorithm can detect incidental bone lesions in CBCT scans with high accuracy, high sensitivity and high precision and is highly recommended for bone lesion detection and follow-up in CBCT imaging. Clinical relevance: The increasing number of CBCT scans performed worldwide dictates a clinical need for developing an automated tool, which will review, detect and volumetrically demonstrate incidental bone lesions in CBCT scans with high accuracy and low false-positive rate.

Published
2022

19. Deep learning algorithm for the detection and 3D segmentation of maxillofacial bone lesions in CBCT

Author

Talia Yeshua, Shmuel Ladyzhensky, Amal Abu-Nasser, Ragda Abdalla-Aslan, Tami Boharon, Avital Itzhak-Pur, Asher Alexander, Akhilanand Chaurasia, Isaac Leichter, and Chen Nadler

Abstract

Objectives: This study aimed to develop an automated deep-learning algorithm for the detection and 3D segmentation of incidental jaw lesions in maxillofacial CBCT scans.Materials and Methods: The dataset included 82 CBCT scans with and without histologically confirmed benign bone lesions, obtained from three CBCT devices using different imaging protocols. The dataset consisted of axial CBCT images and was divided into training dataset (20,214 axial images), validation dataset (4,530 axial images) and testing dataset (6,795 axial images). A Mask-RCNN based deep-learning algorithm segmented the bone lesion in each axial image. The analysis of sequential slices improved the mask RCNN performance and assisted in classifying each CBCT case as containing bone lesions or not. Thereafter, the algorithm generated a 3D segmentation of the lesions. Results: The accuracy of the algorithm for classifying each CBCT case as either containing bone lesions or not, was 100%. The algorithm performance for detecting lesions in individual axial images showed high sensitivity (95.9%) and high precision (98.9%).Conclusions: Our deep learning algorithm can detect incidental bone lesions in CBCT scans with high accuracy, high sensitivity and high precision and is highly recommended for bone lesion detection and follow-up in CBCT imaging. Clinical relevance: The increasing number of CBCT scans performed worldwide dictates a clinical need for developing an automated tool, which will review, detect and volumetrically demonstrate incidental bone lesions in CBCT scans with high accuracy and low false-positive rate.

Published
2022

29. A Novel Nomogram for Peritoneal Mesothelioma Predicts Survival

Author

Schaub, Nicholas P., Alimchandani, Meghna, Quezado, Martha, Kalina, Phil, Eberhardt, John S., Hughes, Marybeth S., Beresnev, Tatiana, Hassan, Raffit, Bartlett, David L., Libutti, Steven K., Pingpank, James F., Royal, Richard E., Kammula, Udai S., Pandalai, Prakash, Phan, Giao Q., Stojadinovic, Alexander, Rudloff, Udo, Alexander, H. Richard, and Avital, Itzhak

Published
2013
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35. Label-retaining liver cancer cells are relatively resistant to sorafenib

Author

Xin, Hong-Wu, Ambe, Chenwi M, Hari, Danielle M, Wiegand, Gordon W, Miller, Tyler C, Chen, Jin-Qiu, Anderson, Andrew J, Ray, Satyajit, Mullinax, John E, Koizumi, Tomotake, Langan, Russell C, Burka, Douglas, Herrmann, Michelle A, Goldsmith, Paul K, Stojadinovic, Alexander, Rudloff, Udo, Thorgeirsson, Snorri S, and Avital, Itzhak

Published
2013
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38. Hypothermic machine perfusion can safely prolong cold ischemia time in deceased donor kidney transplantation. A retrospective analysis on postoperative morbidity and graft function

Author

Adani, Gian Luigi, primary, Pravisani, Riccardo, additional, Tulissi, Patrizia, additional, Isola, Miriam, additional, Calini, Giacomo, additional, Terrosu, Giovanni, additional, Boscutti, Giuliano, additional, Avital, Itzhak, additional, Ekser, Burcin, additional, and Baccarani, Umberto, additional

Published
2021
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