{"id":3505,"date":"2025-11-19T11:12:58","date_gmt":"2025-11-19T10:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/lung-cancer-recurrence-simulator\/"},"modified":"2026-02-11T10:42:28","modified_gmt":"2026-02-11T09:42:28","slug":"lung-cancer-recurrence-simulator","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/lung-cancer-recurrence-simulator\/","title":{"rendered":"Relapse prediction simulator in lung cancer using machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3505\" class=\"elementor elementor-3505\" data-elementor-post-type=\"learning\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-45d90d9a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"45d90d9a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a7fab43\" data-id=\"a7fab43\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6360cb02 elementor-widget elementor-widget-text-editor\" data-id=\"6360cb02\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section><p data-start=\"306\" data-end=\"400\">We developed a machine learning model to evaluate the risk of relapse in lung cancer patients.<\/p><p data-start=\"402\" data-end=\"618\">To build this model, we used mutational data from lung cancer patients, as described in <a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/lung-cancer-recurrence\">Evaluate the probability of relapse in patients with lung cancer<\/a>.\u00a0We calculated the expression values as log2(expression + 1).<\/p><\/section><section><h2>Patient&#8217;s data:<\/h2><\/section><section><div style=\"display: block; text-align: center;\" align=\"center\"><p><!-- MENU OPTIONS HERE --><\/p><form style=\"display: inline-block; margin-left: auto; margin-right: auto; text-align: left;\"><table class=\"form\" border=\"1px\"><tbody><tr style=\"height: 3.5em;\"><td>Pathological nodes:<\/td><td><select id=\"pathological_nodes\" class=\"minimalwhite\" style=\"text-align: right; padding-right: 15%;\" name=\"pathological_nodes\"><option value=\"0\">0<\/option><option value=\"1\">1<\/option><option value=\"2\">2<\/option><\/select><\/td><\/tr><tr style=\"height: 3.5em;\"><td>Pathological tumour:<\/td><td><select id=\"pathological_tumour\" class=\"minimalwhite\" style=\"text-align: right; padding-right: 15%;\" name=\"pathological_tumour\"><option value=\"1\">1<\/option><option value=\"2\">2<\/option><option value=\"3\">3<\/option><option value=\"4\">4<\/option><\/select><\/td><\/tr><tr style=\"height: 3.5em;\"><td>RAD51 expression:<\/td><td style=\"text-align: center;\"><input id=\"rad51\" max=\"2.71\" min=\"2.45\" step=\"0.01\" type=\"range\" value=\"2.45\" \/><br \/><input id=\"rad51_text\" class=\"tabla\" max=\"2.71\" min=\"2.45\" step=\"0.01\" type=\"number\" value=\"2.45\" \/><\/td><\/tr><tr style=\"height: 3.5em;\"><td>ADGRF5 expression:<\/td><td style=\"text-align: center;\"><input id=\"adgrf5\" max=\"3.72\" min=\"2.84\" step=\"0.01\" type=\"range\" value=\"2.84\" \/><br \/><input id=\"adgrf5_text\" class=\"tabla\" max=\"3.72\" min=\"2.84\" step=\"0.01\" type=\"number\" value=\"2.84\" \/><\/td><\/tr><tr style=\"height: 3.5em;\"><td>COCH expression:<\/td><td style=\"text-align: center;\"><input id=\"coch\" max=\"3.48\" min=\"2.61\" step=\"0.01\" type=\"range\" value=\"2.61\" \/><br \/><input id=\"coch_text\" class=\"tabla\" max=\"3.48\" min=\"2.61\" step=\"0.01\" type=\"number\" value=\"2.61\" \/><\/td><\/tr><tr style=\"height: 3.5em;\"><td>SLC2A1 expression:<\/td><td style=\"text-align: center;\"><input id=\"slc2a1\" max=\"3.41\" min=\"2.96\" step=\"0.01\" type=\"range\" value=\"2.96\" \/><br \/><input id=\"slc2a1_text\" class=\"tabla\" max=\"3.41\" min=\"2.96\" step=\"0.01\" type=\"number\" value=\"2.96\" \/><\/td><\/tr><tr style=\"height: 3.5em;\"><td>CLU expression:<\/td><td style=\"text-align: center;\"><input id=\"clu\" max=\"3.61\" min=\"2.73\" step=\"0.01\" type=\"range\" value=\"2.73\" \/><br \/><input id=\"clu_text\" class=\"tabla\" max=\"3.61\" min=\"2.73\" step=\"0.01\" type=\"number\" value=\"2.73\" \/><\/td><\/tr><tr style=\"height: 3.5em;\"><td>ZDHHC7 expression:<\/td><td style=\"text-align: center;\"><input id=\"zdhhc7\" max=\"3.58\" min=\"3.19\" step=\"0.01\" type=\"range\" value=\"3.19\" \/><br \/><input id=\"zdhhc7_text\" class=\"tabla\" max=\"3.58\" min=\"3.19\" step=\"0.01\" type=\"number\" value=\"3.19\" \/><\/td><\/tr><tr style=\"height: 3.5em;\"><td>LRFN4 expression:<\/td><td style=\"text-align: center;\"><input id=\"lrfn4\" max=\"3.36\" min=\"2.81\" step=\"0.01\" type=\"range\" value=\"2.81\" \/><br \/><input id=\"lrfn4_text\" class=\"tabla\" max=\"3.36\" min=\"2.81\" step=\"0.01\" type=\"number\" value=\"2.81\" \/><\/td><\/tr><tr style=\"height: 3.5em;\"><td>AP2A2 expression:<\/td><td style=\"text-align: center;\"><input id=\"ap2a2\" max=\"3.47\" min=\"3.15\" step=\"0.01\" type=\"range\" value=\"3.15\" \/><br \/><input id=\"ap2a2_text\" class=\"tabla\" max=\"3.47\" min=\"3.15\" step=\"0.01\" type=\"number\" value=\"3.15\" \/><\/td><\/tr><\/tbody><\/table><\/form><\/div><div align=\"center\"><!-- BUTTON HERE --><br \/><button class=\"btn\">Plot chart!<\/button><\/div><div style=\"padding-top: -200px;\" align=\"center\"><div id=\"chart_div_bar\" style=\"display: none;\"><!-- PLOT HERE --><\/div><div id=\"leyenda\" style=\"display: none;\"><style type=\"text\/css\">\n                    .tg {<br \/>                        border-collapse: collapse;<br \/>                        border-spacing: 0;<br \/>                    }<\/p>\n<p>                        .tg td {<br \/>                            border-color: black;<br \/>                            border-style: solid;<br \/>                            border-width: 1px;<br \/>                            font-family: Arial, sans-serif;<br \/>                            font-size: 14px;<br \/>                            overflow: hidden;<br \/>                            padding: 10px 5px;<br \/>                            word-break: normal;<br \/>                        }<\/p>\n<p>                        .tg th {<br \/>                            border-color: black;<br \/>                            border-style: solid;<br \/>                            border-width: 1px;<br \/>                            font-family: Arial, sans-serif;<br \/>                            font-size: 14px;<br \/>                            font-weight: normal;<br \/>                            overflow: hidden;<br \/>                            padding: 10px 5px;<br \/>                            word-break: normal;<br \/>                        }<\/p>\n<p>                        .tg .tg-oe15 {<br \/>                            background-color: #ffffff;<br \/>                            border-color: #ffffff;<br \/>                            text-align: left;<br \/>                            vertical-align: top<br \/>                        }<\/p>\n<p>                        .tg .tg-8jgo {<br \/>                            border-color: #ffffff;<br \/>                            text-align: center;<br \/>                            vertical-align: top<br \/>                        }<\/p>\n<p>                        .tg .tg-m8sa {<br \/>                            background-color: #e74c3c;<br \/>                            border-color: #ffffff;<br \/>                            text-align: left;<br \/>                            vertical-align: top<br \/>                        }<\/p>\n<p>                        .tg .tg-tv4a {<br \/>                            background-color: #2ecc71;<br \/>                            border-color: #ffffff;<br \/>                            text-align: left;<br \/>                            vertical-align: top<br \/>                        }<\/p>\n<p>                        .tg .tg-wnlx {<br \/>                            background-color: #f1c40f;<br \/>                            border-color: #ffffff;<br \/>                            text-align: left;<br \/>                            vertical-align: top<br \/>                        }<br \/>                <\/style><table class=\"tg\"><thead><tr><th class=\"tg-8jgo\" colspan=\"9\"><b>Risk of recurrence<\/b><\/th><\/tr><\/thead><tbody><tr><td class=\"tg-tv4a\" colspan=\"2\"><span style=\"color: #2ecc71;\">aaaa<\/span><\/td><td class=\"tg-oe15\">Low<\/td><td class=\"tg-wnlx\" colspan=\"2\"><span style=\"color: #f1c40f;\">aaaa<\/span><\/td><td class=\"tg-oe15\">Medium<\/td><td class=\"tg-m8sa\" colspan=\"2\"><span style=\"color: #e74c3c; background-color: #e74c3c;\">aaaa<\/span><\/td><td class=\"tg-oe15\">High<\/td><\/tr><\/tbody><\/table><div>The color division is made using discrete statistics for the probability of recurrence for each month. Low risk is comprised of values below the first quartile, medium risk of the values between the first quartile and the median, and high risk of values above the median. The median is represented in the figure by the grey line.<\/div><\/div><div id=\"chart_div\" style=\"display: none;\"><!-- PLOT HERE --><\/div><div id=\"explaining\" style=\"display: none;\">The gray line is the percentage of patients with recurrence for a given month. A patient with a line below it would have a lower risk than the population for each month.<\/div><\/div><\/section><section><section><p data-start=\"620\" data-end=\"967\">Next, we divided risk levels using discrete statistics based on the probability of recurrence for each month. Specifically, values below the first quartile represent low risk, values between the first quartile and the median represent medium risk, and values above the median represent high risk. In the figure, the grey line indicates the median.<\/p><p data-start=\"969\" data-end=\"1199\">Additionally, the grey line shows the percentage of patients with recurrence for a given month. Therefore, if a patient\u2019s curve remains below this line, that patient has a lower risk than the overall population at each time point.<\/p><p data-start=\"1201\" data-end=\"1338\">We obtained the expression values from Affymetrix HG-U133A arrays and normalized them using the RMA (Robust Multiarray Averaging) method.<\/p><p data-start=\"1340\" data-end=\"1558\">However, it is important to emphasize that no model can predict the future with certainty. For this reason, physicians must always interpret these predictions within the full clinical context before making a diagnosis.<\/p><\/section><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":1944,"template":"","categories":[29],"tags":[38],"class_list":["post-3505","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-healthcare"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Relapse prediction simulator in lung cancer using machine learning<\/title>\n<meta name=\"description\" content=\"Use a machine learning model to evaluate the probability of relapse in lung cancer patients using mutational data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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