scholarly journals Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 4164
Author(s):  
Gabriele Madonna ◽  
Giuseppe V. Masucci ◽  
Mariaelena Capone ◽  
Domenico Mallardo ◽  
Antonio Maria Grimaldi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metastatic Melanoma ◽  
Learning Algorithm ◽  
Checkpoint Inhibitors ◽  
Real Life ◽  
Previous Treatment ◽  
Clinical Decision ◽  
Neutrophil Lymphocyte Ratio ◽  
Real World Evidence ◽  
Melanoma Patients

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.

scholarly journals Improved Progression-Free Long-Term Survival of a Nation-Wide Patient Population with Metastatic Melanoma

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2591
Author(s):  
Anne Vest Soerensen ◽  
Eva Ellebaek ◽  
Lars Bastholt ◽  
Henrik Schmidt ◽  
Marco Donia ◽  
...  
Keyword(s):  
Metastatic Melanoma ◽  
Checkpoint Inhibitors ◽  
Real Life ◽  
Standard Of Care ◽  
Braf Inhibitors ◽  
Term Survival ◽  
Long Term Survival ◽  
Real World Evidence ◽  
Long Term Survivors

Approval of immune checkpoint-inhibitors (ICIs) and BRAF-inhibitors has revolutionized the treatment of metastatic melanoma. Although these drugs have improved overall survival (OS) in clinical trials, real-world evidence for improved long-term survival is still scarce. Clinical data were extracted from the Danish Metastatic Melanoma database. This nation-wide cohort contains data on all patients who received systemic treatment for metastatic melanoma between 2008 and 2016. Ipilimumab, the first approved ICI, was implemented as standard-of-care in Denmark in 2012. Hence, patients were divided in a pre-ICI (2008–2011) and an ICI (2012–2016) era. Patients were defined as long-term survivors if they were alive 3 years after initiation of systemic therapy. Data from 1754 patients were retrieved. Patients treated in the ICI era had an improved median OS (11.3 months, 95% confidence interval (CI) 10.3–12.3) compared with those in the pre-ICI era (median OS 8.3 months, 95% CI 7.4–9.5, p < 0.0001). A higher proportion of long-term survivors was observed in the ICI era (survivors >3 years increased from 13% to 26% and survivors >5 years increased from 9% to 21%; both p < 0.0001). For long-term survivors, known prognostic factors were equally distributed between the two periods, except that long-term survivors in the pre-ICI era were younger. For long-term survivors, 70% were without progression in the ICI era compared with 43% in the pre-ICI era (p < 0.0001). For all patients, the proportion without progression increased from 5% to 18% between the pre-ICI and the ICI era (p < 0.0001), respectively. Implementation of ICI has led to a significant increase in progression-free, long-term survival for real-life patients with metastatic melanoma.

scholarly journals High alert drugs screening using gradient boosting classifier

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pakpoom Wongyikul ◽  
Nuttamon Thongyot ◽  
Pannika Tantrakoolcharoen ◽  
Pusit Seephueng ◽  
Piyapong Khumrin
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Drug Prescription ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Negative Consequences ◽  
Inappropriate Use ◽  
Prescription Errors ◽  
Screening Protocol

AbstractPrescription errors in high alert drugs (HAD), a group of drugs that have a high risk of complications and potential negative consequences, are a major and serious problem in medicine. Standardized hospital interventions, protocols, or guidelines were implemented to reduce the errors but were not found to be highly effective. Machine learning driven clinical decision support systems (CDSS) show a potential solution to address this problem. We developed a HAD screening protocol with a machine learning model using Gradient Boosting Classifier and screening parameters to identify the events of HAD prescription errors from the drug prescriptions of out and inpatients at Maharaj Nakhon Chiang Mai hospital in 2018. The machine learning algorithm was able to screen drug prescription events with a risk of HAD inappropriate use and identify over 98% of actual HAD mismatches in the test set and 99% in the evaluation set. This study demonstrates that machine learning plays an important role and has potential benefit to screen and reduce errors in HAD prescriptions.

Complete responses to two different anti-PD1 agents in a metastatic melanoma patient

2019 ◽  
Vol 26 (2) ◽  
pp. 496-499 ◽  
Author(s):  
Saadettin Kilickap ◽  
Deniz C Guven ◽  
Oktay H Aktepe ◽  
Burak Y Aktas ◽  
Omer Dizdar
Keyword(s):  
Metastatic Melanoma ◽  
Checkpoint Inhibitors ◽  
Real Life ◽  
Complete Response ◽  
Duration Of Treatment ◽  
Treatment Protocols ◽  
Real Life Data ◽  
Heavily Pretreated ◽  
Drug Free

In the last decade, immune checkpoint inhibitors changed the landscape of metastatic melanoma. However, the optimal duration of treatment and treatment cessation in responders is largely unknown. Herein, we represent a heavily pretreated metastatic melanoma case who had a complete response to pembrolizumab and also a complete response with nivolumab after progression during drug-free follow-up. We think that reinduction with a different anti-PD1 antibody may be used in patients with metastatic melanoma responders. Clinical trials with prespecified sequential treatment protocols and large real-life data can further delineate this subject.

Predictive Data Modeling Using sp-kNN for Risk Factor Evaluation in Urban Demographical Healthcare Data

2021 ◽  
Vol 11 (1) ◽  
pp. 7-14
Author(s):  
Uzair Aslam Bhatti ◽  
Linwang Yuan ◽  
Zhaoyuan Yu ◽  
Saqib Ali Nawaz ◽  
Anum Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Eye Diseases ◽  
Machine Learning Algorithms ◽  
Medical Resources ◽  
Individual Level ◽  
Healthcare Data ◽  
Increasing Demand

Healthcare diseases are spreading all around the globe day to day. Hospital datasets are full from the data with much information. It's an urgent requirement to use that data perfectly and efficiently. We propose a novel algorithm for predictive model for eye diseases using KNN with machine learning algorithms and artificial intelligence (AI). The aims are to evaluate the connection between the accumulated preoperative risk variables and different eye diseases and to manufacture a model that can anticipate the results on an individual level, thus giving relevance to impactful factors and geographic and demographic features. Risk factors of the desired diseases were calculated and machine learning algorithm applied to provide the prediction of the diseases. Health monitoring is an economic discipline that focuses on the effective allocation of medical resources, mainly to maximize the benefits of society to health through the available resources. With the increasing demand for medical services and the limited allocation of medical resources, the application of health economics in clinical practice has been paid more and more attention, and it has gradually played an important role in clinical decision-making.

Prognostic relevance of neutrophil to lymphocyte ratio (NLR) before anti-PD1 therapy in metastatic melanoma patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e21045-e21045
Author(s):  
Daniel Vilarim Araujo ◽  
Rafael Vanin de Moraes ◽  
Victor Aurelio Ramos Sousa ◽  
Mauro Daniel Spina Donadio ◽  
Aline Fusco Fares ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Metastatic Melanoma ◽  
Proportional Hazards ◽  
Checkpoint Inhibitors ◽  
Prognostic Significance ◽  
Multivariable Analysis ◽  
Continuous Variable ◽  
Cox Proportional Hazards ◽  
Rank Test ◽  
Melanoma Patients

e21045 Background: Biomarkers to select the patients most likely to benefit from checkpoint inhibitors are urged. NLR is a simple way of measuring systemic inflammation and is an independent predictor of survival before Anti-CTLA4 therapy. We hypothesized if NLR is also a predictor of survival before Anti-PD1 therapy. Methods: We performed a retrospective review of the medical records of all consecutive metastatic melanoma patients who received Nivolumab treatment from January/2014 – February/2017, including 53 patients prospectively collected from an Expanded Access Program. Of 86 patients, 83 patients were included for demographic and efficacy analysis, and 74 had information about baseline pre-treatment NLR. We analyzed NLR as a continuous variable and categorised ≥ 5 vs. < 5. Kaplan-Meier method was used for survival analysis. Long-rank test compared categories and Cox proportional hazards regression model was used to assess the prognostic significance of baseline NLR in univariate and multivariable analysis. Results: Median PFS for the entire population was 6,407 months (3,28 – 9,52) and median OS was not reached (NR) with a median FU of 10,74 months. The median NLR ratio was 3,11 (0,87 – 19). 18 patients (24,3%) had a ≥ 5 NLR vs. 56 (75,7%) < 5. Median PFS for NLR ≥ 5 was: 2,3 (1,75 – 2,84) vs. 12,02 (5,11 – 18,93) for < 5 (HR = 3,11; IC95% 1,52 – 6,27; p = 0,001). Median OS ≥ 5: 3,05 (2,06 – 4,04) vs. NR for < 5 (HR = 5,88; IC95% 2,60 – 13,29; p = 0,001). NLR categorised remained statistically significant in multivariate analysis for PFS and NLR as a continuous variable remained statistically significant for both PFS and OS in multivariate analysis (Table 1). Conclusions: Baseline NLR is a rapid, simple, and cost-free predictor of survival before Anti-PD1 therapy. These results should be validated in a larger cohort of patients. [Table: see text]

Using machine learning algorithms to predict response and toxicity to immune checkpoint inhibitors (ICIs) in melanoma patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 2581-2581 ◽  
Author(s):  
Paul Johannet ◽  
Nicolas Coudray ◽  
George Jour ◽  
Douglas MacArthur Donnelly ◽  
Shirin Bajaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Predictive Accuracy ◽  
Checkpoint Inhibitors ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Melanoma Patients

2581 Background: There is growing interest in optimizing patient selection for treatment with immune checkpoint inhibitors (ICIs). We postulate that phenotypic features present in metastatic melanoma tissue reflect the biology of tumor cells, immune cells, and stromal tissue, and hence can provide predictive information about tumor behavior. Here, we test the hypothesis that machine learning algorithms can be trained to predict the likelihood of response and/or toxicity to ICIs. Methods: We examined 124 stage III/IV melanoma patients who received anti-CTLA-4 (n = 81), anti-PD-1 (n = 25), or combination (n = 18) therapy as first line. The tissue analyzed was resected before treatment with ICIs. In total, 340 H&E slides were digitized and annotated for three regions of interest: tumor, lymphocytes, and stroma. The slides were then partitioned into training (n = 285), validation (n = 26), and test (n = 29) sets. Slides were tiled (299x299 pixels) at 20X magnification. We trained a deep convolutional neural network (DCNN) to automatically segment the images into each of the three regions and then deconstruct images into their component features to detect non-obvious patterns with objectivity and reproducibility. We then trained the DCNN for two classifications: 1) complete/partial response versus progression of disease (POD), and 2) severe versus no immune-related adverse events (irAEs). Predictive accuracy was estimated by area under the curve (AUC) of receiver operating characteristics (ROC). Results: The DCNN identified tumor within LN with AUC 0.987 and within ST with AUC 0.943. Prediction of POD based on ST-only always performed better than prediction based on LN-only (AUC 0.84 compared to 0.61, respectively). The DCNN had an average AUC 0.69 when analyzing only tumor regions from both LN and ST data sets and AUC 0.68 when analyzing tumor and lymphocyte regions. Severe irAEs were predicted with limited accuracy (AUC 0.53). Conclusions: Our results support the potential application of machine learning on pre-treatment histologic slides to predict response to ICIs. It also revealed their limited value in predicting toxicity. We are currently investigating whether the predictive capability of the algorithm can be further improved by incorporating additional immunologic biomarkers.

scholarly journals Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1751
Author(s):  
Emily Z. Ma ◽  
Karl M. Hoegler ◽  
Albert E. Zhou
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Risk Stratification ◽  
Metastatic Melanoma ◽  
The United States ◽  
Clinical Staging ◽  
Machine Learning Applications ◽  
Risk Stratification Tool ◽  
Melanoma Treatment ◽  
Melanoma Patients

Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7,000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.

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