Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Nimgaonkar V
PROVIDER: S-EPMC10140610 | biostudies-literature | 2023 Apr
REPOSITORIES: biostudies-literature
Publications
Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer.
Nimgaonkar Vivek V Krishna Viswesh V Krishna Vrishab V Tiu Ekin E Joshi Anirudh A Vrabac Damir D Bhambhvani Hriday H Smith Katelyn K Johansen Julia S JS Makawita Shalini S Musher Benjamin B Mehta Arnav A Hendifar Andrew A Wainberg Zev Z Sohal Davendra D Fountzilas Christos C Singhi Aatur A Rajpurkar Pranav P Collisson Eric A EA
Cell reports. Medicine 20230411 4
Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic s ...[more]
Similar Datasets
Project description:BackgroundAdjuvant chemotherapy is the standard treatment after curative-intent surgery for pancreatic ductal adenocarcinoma (PDAC). The phase-3 ESPAC-4 trial demonstrated significantly improved overall survival (OS) with Gemcitabine plus capecitabine (GemCap) over Gemcitabine (Gem) in Europe. We conducted a retrospective efficacy and safety evaluation of GemCap versus Gem in an Asian population.MethodsThis retrospective analysis included 292 patients with PDAC who received adjuvant Gem or GemCap after curative resection between January 2017 and December 2020 at Asan Medical Center, Seoul, Korea.ResultsAdjuvant Gem and GemCap were administered to 161 (55.1%) and 131 (44.8%) patients, respectively. The Gem group had significantly older patients (median 66 versus 63 years, p = 0.001); otherwise, the groups had similar baseline characteristics. With median follow-up durations of 39.4 [95% confidence interval (CI), 36.9-45.0] and 39.4 (95% CI, 34.7-41.6) months in the Gem and GemCap groups, the median OS was 36.8 (95% CI, 29.7-43.5) and 46.1 (95% CI, 31.5-not reached) months in the Gem and GemCap groups, respectively [unadjusted hazard ratio (HR) = 0.7; 95% CI, 0.5-1.0; p = 0.07). The median recurrence-free survival was 14.3 (95% CI, 12.9-17.7) and 17.0 (95% CI, 13.3-28.2) months, respectively (p = 0.5). Hand-foot skin reactions (any grade, 15.3% versus 0.6%; p < 0.001), neutropenia (78.6% versus 67.7%, p = 0.04) and thrombocytopenia (30.5% versus 20.5%, p = 0.04) were more common in the GemCap group. Multivariate analysis revealed adjuvant GemCap - compared with Gem - to be significantly associated with better OS (adjusted HR = 0.6; 95% CI, 0.4-0.9; p = 0.01). Otherwise, moderate or poor histological grade, lymph node positivity, positive resection margin, and elevated CA 19-9 (>median) were significantly associated with worse OS.ConclusionsAdjuvant GemCap showed the consistent clinical outcomes with the ESPAC-4 trial. As mFOLFIRINOX is the new standard treatment for medically fit patients with resected PDAC, further evaluation of optimal adjuvant chemotherapy in daily practice is warranted.
| S-EPMC9092583 | biostudies-literature
Project description:BackgroundDespite major improvements in the perioperative outcome of pancreas surgery, the prognosis of pancreatic cancer after curative resection remains poor. Adjuvant chemotherapy increases disease-free and overall survival, but this treatment cannot be offered to a significant proportion of patients due to the surgical morbidity. In contrast, almost all patients can receive (neo)adjuvant chemotherapy before surgery. This treatment is safe and effective, and has resulted in a median survival of 26.5 months in a recent phase II trial. Moreover, neoadjuvant chemotherapy improves the nutritional status of patients with pancreatic cancer. This multicenter phase III trial (NEOPAC) has been designed to explore the efficacy of neoadjuvant chemotherapy.Methods/designThis is a prospective randomized phase III trial. Patients with resectable cytologically proven adenocarcinoma of the pancreatic head are eligible for this study. All patients must be at least 18 years old and must provide written informed consent. An infiltration of the superior mesenteric vein > 180° or major visceral arteries are considered exclusion criteria. Eligible patients will be randomized to surgery followed by adjuvant gemcitabine (1000 mg/m(2)) for 6 months or neoadjuvant chemotherapy (gemcitabine 1000 mg/m(2), oxaliplatin 100 mg/m(2)) followed by surgery and the same adjuvant treatment. Neoadjuvant chemotherapy is given four times every two weeks. The staging as well as the restaging protocol after neoadjuvant chemotherapy include computed tomography of chest and abdomen and diagnostic laparoscopy. The primary study endpoint is progression-free survival. According to the sample size calculation, 155 patients need to be randomized to each treatment arm. Disease recurrence will be documented by scheduled computed tomography scans 9, 12, 15, 21 and thereafter every 6 months until disease progression. For quality control, circumferential resection margins are marked intraoperatively, and representative histological sections will be centrally reviewed by a dedicated pathologist.DiscussionThe NEOPAC study will determine the efficacy of neoadjuvant chemotherapy in pancreatic cancer for the first time and offers a unique potential for translational research. Furthermore, this trial will provide the unbiased overall survival of all patients undergoing surgery for resectable cancer of the pancreatic head.Trial registrationclinicalTrials.gov NCT01314027.
| S-EPMC3176241 | biostudies-literature
Project description:ImportanceEarly results at 3 years from the PRODIGE 24/Canadian Cancer Trials Group PA6 randomized clinical trial showed survival benefits with adjuvant treatment with modified FOLFIRINOX vs gemcitabine in patients with resected pancreatic ductal adenocarcinoma; mature data are now available.ObjectiveTo report 5-year outcomes and explore prognostic factors for overall survival.Design, setting, and participantsThis open-label, phase 3 randomized clinical trial was conducted at 77 hospitals in France and Canada and included patients aged 18 to 79 years with histologically confirmed pancreatic ductal adenocarcinoma who had undergone complete macroscopic (R0/R1) resection within 3 to 12 weeks before randomization. Patients were included from April 16, 2012, through October 3, 2016. The cutoff date for this analysis was June 28, 2021.InterventionsA total of 493 patients were randomized (1:1) to receive treatment with modified FOLFIRINOX (oxaliplatin, 85 mg/m2 of body surface area; irinotecan, 150-180 mg/m2; leucovorin, 400 mg/m2; and fluorouracil, 2400 mg/m2, every 2 weeks) or gemcitabine (1000 mg/m2, days 1, 8, and 15, every 4 weeks) as adjuvant therapy for 24 weeks.Main outcomes and measuresPrimary end point was disease-free survival. Secondary end points included overall survival, metastasis-free survival, and cancer-specific survival. Prognostic factors for overall survival were determined.ResultsOf the 493 patients, 216 (43.8%) were women, and the mean (SD) age was 62.0 (8.9) years. At a median of 69.7 months' follow-up, 367 disease-free survival events were observed. In patients receiving chemotherapy with modified FOLFIRINOX vs gemcitabine, median disease-free survival was 21.4 months (95% CI, 17.5-26.7) vs 12.8 months (95% CI, 11.6-15.2) (hazard ratio [HR], 0.66; 95% CI, 0.54-0.82; P < .001) and 5-year disease-free survival was 26.1% vs 19.0%; median overall survival was 53.5 months (95% CI, 43.5-58.4) vs 35.5 months (95% CI, 30.1-40.3) (HR, 0.68; 95% CI, 0.54-0.85; P = .001), and 5-year overall survival was 43.2% vs 31.4%; median metastasis-free survival was 29.4 months (95% CI, 21.4-40.1) vs 17.7 months (95% CI, 14.0-21.2) (HR, 0.64; 95% CI, 0.52-0.80; P < .001); and median cancer-specific survival was 54.7 months (95% CI, 45.8-68.4) vs 36.3 months (95% CI, 30.5-43.9) (HR, 0.65; 95% CI, 0.51-0.82; P < .001). Multivariable analysis identified modified FOLFIRINOX, age, tumor grade, tumor staging, and larger-volume center as significant favorable prognostic factors for overall survival. Shorter relapse delay was an adverse prognostic factor.Conclusions and relevanceThe final 5-year results from the PRODIGE 24/Canadian Cancer Trials Group PA6 randomized clinical trial indicate that adjuvant treatment with modified FOLFIRINOX yields significantly longer survival than gemcitabine in patients with resected pancreatic ductal adenocarcinoma.Trial registrationEudraCT: 2011-002026-52; ClinicalTrials.gov Identifier: NCT01526135.
| S-EPMC9437831 | biostudies-literature
Project description:BackgroundEndoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos.Methods1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured.ResultsThe AI system detected ER (UCEIS ≤ 1) in WLE videos with 72 % sensitivity, 87 % specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO ≤ 3), the sensitivity was 79 %, specificity 95 %, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80 % to 85 %). The model's stratification of risk of flare was similar to that of physician-assessed endoscopy scores.ConclusionsOur system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment.
| S-EPMC10060056 | biostudies-literature
Project description:The added value of capecitabine to adjuvant gemcitabine monotherapy (GEM) in pancreatic ductal adenocarcinoma (PDAC) was shown by the ESPAC-4 trial. Real-world data on the effectiveness of gemcitabine plus capecitabine (GEMCAP), in patients ineligible for mFOLFIRINOX, are lacking. Our study assessed whether adjuvant GEMCAP is superior to GEM in a nationwide cohort. Patients treated with adjuvant GEMCAP or GEM after resection of PDAC without preoperative treatment were identified from The Netherlands Cancer Registry (2015-2019). The primary outcome was overall survival (OS), measured from start of chemotherapy. The treatment effect of GEMCAP vs GEM was adjusted for sex, age, performance status, tumor size, lymph node involvement, resection margin and tumor differentiation in a multivariable Cox regression analysis. Secondary outcome was the percentage of patients who completed the planned six adjuvant treatment cycles. Overall, 778 patients were included, of whom 21.1% received GEMCAP and 78.9% received GEM. The median OS was 31.4 months (95% CI 26.8-40.7) for GEMCAP and 22.1 months (95% CI 20.6-25.0) for GEM (HR: 0.71, 95% CI 0.56-0.90; logrank P = .004). After adjustment for prognostic factors, survival remained superior for patients treated with GEMCAP (HR: 0.73, 95% CI 0.57-0.92, logrank P = .009). Survival with GEMCAP was superior to GEM in most subgroups of prognostic factors. Adjuvant chemotherapy was completed in 69.5% of the patients treated with GEMCAP and 62.7% with GEM (P = .11). In this nationwide cohort of patients with PDAC, adjuvant GEMCAP was associated with superior survival as compared to GEM monotherapy and number of cycles was similar.
| S-EPMC9303436 | biostudies-literature
Project description:Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.
| S-EPMC7898901 | biostudies-literature
Project description:The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.
| S-EPMC11866320 | biostudies-literature
Project description:ImportanceColorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists' classification of colorectal polyps, and improved assessment by pathologists may be associated with reduced subsequent underuse and overuse of colonoscopy.ObjectiveTo compare standard microscopic assessment with an artificial intelligence (AI)-augmented digital system that annotates regions of interest within digitized polyp tissue and predicts polyp type using a deep learning model to assist pathologists in colorectal polyp classification.Design, setting, and participantsIn this diagnostic study conducted at a tertiary academic medical center and a community hospital in New Hampshire, 100 slides with colorectal polyp samples were read by 15 pathologists using a microscope and an AI-augmented digital system, with a washout period of at least 12 weeks between use of each modality. The study was conducted from February 10 to July 10, 2020.Main outcomes and measuresAccuracy and time of evaluation were used to compare pathologists' performance when a microscope was used with their performance when the AI-augmented digital system was used. Outcomes were compared using paired t tests and mixed-effects models.ResultsIn assessments of 100 slides with colorectal polyp specimens, use of the AI-augmented digital system significantly improved pathologists' classification accuracy compared with microscopic assessment from 73.9% (95% CI, 71.7%-76.2%) to 80.8% (95% CI, 78.8%-82.8%) (P < .001). The overall difference in the evaluation time per slide between the digital system (mean, 21.7 seconds; 95% CI, 20.8-22.7 seconds) and microscopic examination (mean, 13.0 seconds; 95% CI, 12.4-13.5 seconds) was -8.8 seconds (95% CI, -9.8 to -7.7 seconds), but this difference decreased as pathologists became more familiar and experienced with the digital system; the difference between the time of evaluation on the last set of 20 slides for all pathologists when using the microscope and the digital system was 4.8 seconds (95% CI, 3.0-6.5 seconds).Conclusions and relevanceIn this diagnostic study, an AI-augmented digital system significantly improved the accuracy of pathologic interpretation of colorectal polyps compared with microscopic assessment. If applied broadly to clinical practice, this tool may be associated with decreases in subsequent overuse and underuse of colonoscopy and thus with improved patient outcomes and reduced health care costs.
| S-EPMC8603082 | biostudies-literature
Project description:Background and aimsWith the development of artificial intelligence (AI), we have become capable of applying real-time computer-aided detection (CAD) in clinical practice. Our aim is to develop an AI-based CAD-N and optimize its diagnostic performance with narrow-band imaging (NBI) images.MethodsWe developed the CAD-N model with ResNeSt using NBI images for real-time assessment of the histopathology of colorectal polyps (type 1, hyperplastic or inflammatory polyps; type 2, adenomatous polyps, intramucosal cancer, or superficial submucosal invasive cancer; type 3, deep submucosal invasive cancer; and type 4, normal mucosa). We also collected 116 consecutive polyp videos to validate the accuracy of the CAD-N.ResultsA total of 10,573 images (7,032 images from 650 polyps and 3,541 normal mucous membrane images) from 478 patients were finally chosen for analysis. The sensitivity, specificity, PPV, NPV, and accuracy for each type of the CAD-N in the test set were 89.86%, 97.88%, 93.13%, 96.79%, and 95.93% for type 1; 93.91%, 95.49%, 91.80%, 96.69%, and 94.94% for type 2; 90.21%, 99.29%, 90.21%, 99.29%, and 98.68% for type 3; and 94.86%, 97.28%, 94.73%, 97.35%, and 96.45% for type 4, respectively. The overall accuracy was 93%. We also built models for polyps ≤5 mm, and the sensitivity, specificity, PPV, NPV, and accuracy for them were 96.81%, 94.08%, 95%, 95.97%, and 95.59%, respectively. Video validation results showed that the sensitivity, specificity, and accuracy of the CAD-N were 84.62%, 86.27%, and 85.34%, respectively.ConclusionsWe have developed real-time AI-based histologic classifications of colorectal polyps using NBI images with good accuracy, which may help in clinical management and documentation of optical histology results.
| S-EPMC9128404 | biostudies-literature
Project description:Gemcitabine resistance is currently the main problem of chemotherapy for advanced pancreatic cancer patients. The resistance is thought to be caused by altered drug metabolism or reduced apoptosis of cancer cells. However, the underlying mechanism of Gemcitabine resistance in pancreatic cancer remains unclear. In this study, we established Gemcitabine resistant PANC-1 (PANC-1-GR) cell lines and compared the circular RNAs (circRNAs) profiles between PANC-1 cells and PANC-1-GR cells by RNA sequencing. Differentially expressed circRNAs were demonstrated using scatter plot and cluster heatmap analysis. Gene ontology and pathway analysis were performed to systemically map the genes which are functionally associated to those differentially expressed circRNAs identified from our data. The expression of the differentially expressed circRNAs picked up by RNAseq in PANC-1-GR cells was further validated by qRT-PCR and two circRNAs were eventually identified as the most distinct targets. Consistently, by analyzing plasma samples form pancreatic ductal adenocarcinoma (PDAC) patients, the two circRNAs showed more significant expression in the Gemcitabine non-responsive patients than the responsive ones. In addition, we found that silencing of the two circRNAs could restore the sensitivity of PANC-1-GR cells to Gemcitabine treatment, while over-expression of them could increase the resistance of normal PANC-1 and MIA PACA-2 cells, suggesting that they might serve as drug targets for Gemcitabine resistance. Furthermore, the miRNA interaction networks were also explored based on the correlation analysis of the target microRNAs of these two circRNAs. In conclusion, we successfully established new PANC-1-GR cells, systemically characterized the circRNA and miRNA profiles, and identified two circRNAs as novel biomarkers and potential therapeutic targets for Gemcitabine non-responsive PDAC patients.
| S-EPMC5996282 | biostudies-literature