{"id":3513,"date":"2025-09-01T11:12:58","date_gmt":"2025-09-01T09:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/pancreatic-cancer\/"},"modified":"2026-09-18T16:07:47","modified_gmt":"2026-09-18T14:07:47","slug":"pancreatic-cancer","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/pancreatic-cancer\/","title":{"rendered":"Pancreatic cancer prediction with machine learning"},"content":{"rendered":"<style data-nd-chart-migration=\"1\">\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle .nd-chart-fallback{display:block;margin:0 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12px;color:#12354b;font-size:20px}.nds-centered-figure{width:min(700px,100%);margin:24px auto;padding:16px;border:1px solid #dce8ef;border-radius:15px;background:#f8fbfd}.nds-centered-figure img{width:100%;max-width:100%;margin:0 auto 12px}.nds-validation-grid{display:grid;grid-template-columns:1.2fr 1fr;gap:20px;align-items:start}.nds-flow{display:grid;grid-template-columns:repeat(6,minmax(0,1fr));gap:10px;margin:24px 0}.nds-flow div{display:flex;min-height:102px;align-items:center;justify-content:center;padding:13px 8px;border-radius:13px;background:#12354b;color:#fff;text-align:center;font-weight:700}.nds-calculator{margin:28px 0;padding:24px;border:1px solid #cfe1eb;border-radius:18px;background:#f7fbfd}.nds-calculator-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:15px}.nds-calculator label{display:flex;flex-direction:column;gap:6px;color:#12354b;font-size:13px;font-weight:700}.nds-calculator input,.nds-calculator select{width:100%;min-height:46px;padding:10px 12px;border:1px solid #b9cfdb;border-radius:9px;background:#fff;color:#173246;font:inherit}.nds-calculator .is-invalid{border-color:#c94747;background:#fff8f8}.nds-calculator-actions{display:flex;flex-wrap:wrap;justify-content:center;gap:10px;margin:20px 0}.nds-calculator button{padding:11px 19px;border:0;border-radius:24px;background:#245e80;color:#fff;font:inherit;font-weight:700;cursor:pointer}.nds-calculator .nds-secondary-action{background:#e1edf3;color:#12354b}.nds-calculator-error{min-height:24px;color:#a12828;text-align:center;font-weight:600}.nds-multiclass-score{display:grid;grid-template-columns:repeat(3,1fr);gap:10px;padding:16px;border:1px solid #d6e4eb;border-radius:13px;background:#fff;text-align:center}.nds-multiclass-score div{padding:12px;border-radius:9px;background:#f4f8fa}.nds-multiclass-score span,.nds-multiclass-score strong{display:block}.nds-multiclass-score strong{margin-top:6px;color:#12354b;font-size:18px}.nds-multiclass-score p{grid-column:1\/-1;margin:4px 0 0}@media(max-width:1000px){.nds-flow{grid-template-columns:repeat(3,1fr)}.nds-validation-grid{grid-template-columns:1fr}}@media(max-width:760px){.nds-flow,.nds-calculator-grid,.nds-multiclass-score{grid-template-columns:1fr}.nds-multiclass-score p{grid-column:auto}}<\/style>\n<div class=\"nds\" data-health-profile=\"diagnosis\">\n<div class=\"nds-wrap\">\n<section class=\"nds-executive\">\n<h2>Classify pancreatic-disease study specimens into three categories<\/h2>\n<p>This reproducible Neural Designer example treats diagnosis as one categorical endpoint: no pancreatic disease, benign hepatobiliary disease or pancreatic ductal adenocarcinoma (PDAC). The fixed softmax model correctly classifies 79 of 118 internally held-out specimens and detects 28 of 32 PDAC labels. It is a research benchmark, not a clinical diagnostic or screening system.<\/p>\n<div class=\"nds-kpis\">\n<div class=\"nds-kpi\"><strong>590<\/strong><span>case-control study specimens<\/span><\/div>\n<div class=\"nds-kpi\"><strong>66.9%<\/strong><span>internal testing accuracy<\/span><\/div>\n<div class=\"nds-kpi\"><strong>87.5%<\/strong><span>PDAC recall (28 of 32)<\/span><\/div>\n<div class=\"nds-kpi\"><strong>0.677<\/strong><span>macro-F1 across three classes<\/span><\/div>\n<\/div>\n<div class=\"nds-actions\"><a href=\"#6-clinical-validation\">Review internal validation<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/pancreatic-cancer.csv\">Review the data<\/a><\/div>\n<\/section>\n<ul class=\"nds-toc\">\n<li><a href=\"#1-intended-use\">Question and use<\/a><\/li>\n<li><a href=\"#2-cohort-endpoint\">Cohort and endpoint<\/a><\/li>\n<li><a href=\"#3-model\">Model<\/a><\/li>\n<li><a href=\"#4-training\">Training<\/a><\/li>\n<li><a href=\"#5-selection\">Selection<\/a><\/li>\n<li><a href=\"#6-clinical-validation\">Validation<\/a><\/li>\n<li><a href=\"#7-workflow\">Workflow<\/a><\/li>\n<li><a href=\"#8-safety\">Safety and validity<\/a><\/li>\n<\/ul>\n<section id=\"1-intended-use\" class=\"nds-card\">\n<h2>1. Clinical question and intended use<\/h2>\n<p>The model assigns each processed urine-specimen record to one of three mutually exclusive study labels. This is the coherent formulation for the supplied endpoint: a single categorical target and one three-score softmax output, rather than separate models for each category.<\/p>\n<p>Its defensible use is reproducible biomarker-method research and software education. It may help investigators understand an end-to-end multiclass workflow, but it cannot determine whether an individual patient has pancreatic cancer.<\/p>\n<div class=\"nds-value-grid\">\n<div class=\"nds-value\">\n<h3>One clinical question<\/h3>\n<p>Distinguish no pancreatic disease, benign hepatobiliary disease and PDAC in one categorical experiment.<\/p>\n<\/div>\n<div class=\"nds-value\">\n<h3>Auditable cleaning<\/h3>\n<p>Remove variables with more than 45% missing values before fitting the model and document what remains.<\/p>\n<\/div>\n<div class=\"nds-value\">\n<h3>Reproducible calculation<\/h3>\n<p>Inspect the exact split, encoded input contract, coefficients and three returned scores.<\/p>\n<\/div>\n<\/div>\n<div class=\"nds-audience\"><span>Biomarker research<\/span><span>Clinical data science<\/span><span>Laboratory medicine<\/span><span>Biostatistics<\/span><span>Translational oncology<\/span><\/div>\n<div class=\"nds-note nds-use-boundary\"><strong>Intended-use boundary.<\/strong> This example is a retrospective case-control research demonstration. It is not validated for population screening, differential diagnosis, clinical triage or treatment decisions.<\/div>\n<\/section>\n<section id=\"2-cohort-endpoint\" class=\"nds-card\">\n<h2>2. Cohort, measurements and endpoint<\/h2>\n<p>The data accompany a multicentre case-control biomarker study and contain 590 specimens: 183 with no pancreatic disease, 208 with benign hepatobiliary disease and 199 with PDAC. The project uses a reproducible random 60\/20\/20 row split: 354 training, 118 selection and 118 testing records.<\/p>\n<figure class=\"nds-centered-figure\">\n<div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-3513-00\">\n<div class=\"nd-chart-host\" id=\"nd-3513-00\" role=\"img\" aria-label=\"Interactive chart: Distribution of the three pancreatic-disease study labels\"><\/div>\n<p><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/pancreatic-cancer-class-distribution-2026.png\" alt=\"Distribution of the three pancreatic-disease study labels\"><script type=\"application\/json\" id=\"nd-3513-00-data\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"benign hepatobilliary disease\",\"no pancreatic disease\",\"pancreatic ductal adenocarcinoma\"],\"marker\":{\"colors\":[\"#6abfea\",\"#1879aa\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[35.29999923706055,31,33.70000076293945]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"diagnosis distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>The classes are relatively balanced in this research collection. Their proportions are not population prevalence estimates.<\/figcaption><\/figure>\n<h3>Missing-data cleaning<\/h3>\n<p>Neural Designer&#8217;s <em>Unuse missing variables<\/em> task applies a 45% threshold before modelling. It removes the following columns:<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Variable<\/th>\n<th>Missing rows<\/th>\n<th>Missing rate<\/th>\n<th>Decision<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th><code>stage<\/code><\/th>\n<td>391<\/td>\n<td>66.3%<\/td>\n<td>Unused<\/td>\n<\/tr>\n<tr>\n<th><code>benign_sample_diagnosis<\/code><\/th>\n<td>382<\/td>\n<td>64.7%<\/td>\n<td>Unused<\/td>\n<\/tr>\n<tr>\n<th><code>REG1A<\/code><\/th>\n<td>284<\/td>\n<td>48.1%<\/td>\n<td>Unused<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><code>sample_id<\/code> is also excluded because it is an identifier. <code>plasma_CA19_9<\/code> remains available despite 240 missing values (40.7%), and the stored project replaces missing entries with the mean 654.003. The resulting model uses nine logical predictors, expanded to 12 numeric inputs after categorical encoding.<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Predictor<\/th>\n<th>Role<\/th>\n<th>Processing<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th><code>patient_cohort<\/code><\/th>\n<td>Study provenance<\/td>\n<td>Binary encoding<\/td>\n<\/tr>\n<tr>\n<th><code>sample_origin<\/code><\/th>\n<td>Collection site<\/td>\n<td>Four one-hot inputs<\/td>\n<\/tr>\n<tr>\n<th><code>age<\/code>, <code>sex<\/code><\/th>\n<td>Demographics<\/td>\n<td>Numeric\/binary scaling<\/td>\n<\/tr>\n<tr>\n<th><code>plasma_CA19_9<\/code><\/th>\n<td>Blood biomarker<\/td>\n<td>Mean imputation and scaling<\/td>\n<\/tr>\n<tr>\n<th><code>creatinine<\/code>, <code>LYVE1<\/code>, <code>REG1B<\/code>, <code>TFF1<\/code><\/th>\n<td>Urine measurements<\/td>\n<td>Numeric scaling<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<figure class=\"nds-centered-figure\">\n<div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-3513-01\">\n<div class=\"nd-chart-host\" id=\"nd-3513-01\" role=\"img\" aria-label=\"Interactive chart: Input-target Pearson correlations for the retained pancreatic-cancer predictors\"><\/div>\n<p><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/pancreatic-cancer-input-target-correlations-2026.png\" alt=\"Input-target Pearson correlations for the retained pancreatic-cancer predictors\"><script type=\"application\/json\" id=\"nd-3513-01-data\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"orientation\":\"h\",\"showlegend\":false,\"type\":\"bar\",\"x\":[0.06459999829530716,0.12600000202655792,0.25999999046325684,0.2840000092983246,0.3319999873638153,0.37299999594688416,0.4180000126361847,0.4180000126361847,0.6190000176429749],\"y\":[\"creatinine\",\"sex\",\"patient_cohort\",\"age\",\"REG1B\",\"TFF1\",\"sample_origin\",\"LYVE1\",\"plasma_CA19_9\"]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"diagnosis Pearson correlations chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-1,1],\"title\":{\"text\":\"Correlation\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"autorange\":\"reversed\",\"categoryarray\":[\"creatinine\",\"sex\",\"patient_cohort\",\"age\",\"REG1B\",\"TFF1\",\"sample_origin\",\"LYVE1\",\"plasma_CA19_9\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Plasma CA19-9 has the largest displayed univariate association. Correlation with an encoded multiclass target is descriptive and depends on class coding; it is not a causal importance measure.<\/figcaption><\/figure>\n<div class=\"nds-note nds-note--provenance\"><strong>Provenance and leakage boundary.<\/strong> <code>stage<\/code> is available mainly for cancer cases and is outcome-adjacent, so excluding it also avoids an obvious shortcut. More subtly, cohort and collection site can encode centre effects. A professional validation should hold out complete sites or cohorts and test a biomarker-only specification.<\/div>\n<\/section>\n<section id=\"3-model\" class=\"nds-card\">\n<h2>3. Model<\/h2>\n<p>The fixed architecture has no hidden layer. Nine logical predictors become 12 numeric features after encoding cohort, origin and sex; a direct dense layer returns three softmax scores. With 36 weights and three biases, the model has 39 trainable parameters and is equivalent to multinomial logistic regression.<\/p>\n<div class=\"nds-note\"><strong>Output contract.<\/strong> The three scores correspond to benign hepatobiliary disease, no pancreatic disease and PDAC, and the largest score supplies the categorical prediction. The scores sum to one but have not been independently calibrated as clinical probabilities.<\/div>\n<figure class=\"nds-architecture-figure\" data-model-stage=\"initial\"><img decoding=\"async\" class=\"nds-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/pancreatic-cancer-network-architecture-2026.png\" alt=\"Fixed pancreatic-cancer categorical architecture with nine logical inputs and one three-class target\"><figcaption>Fixed base and final architecture. The diagram shows the categorical diagnosis as one logical output; the exported implementation returns three softmax scores.<\/figcaption><\/figure>\n<\/section>\n<section id=\"4-training\" class=\"nds-card\">\n<h2>4. Training strategy<\/h2>\n<p>The training strategy minimizes multiclass cross-entropy with the quasi-Newton method and L2 regularization weight 0.01. Across 35 stored epochs, training cross-entropy falls from about 1.170 to 0.660 and selection cross-entropy from about 0.855 to 0.734.<\/p>\n<figure class=\"nds-centered-figure\">\n<div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-3513-03\">\n<div class=\"nd-chart-host\" id=\"nd-3513-03\" role=\"img\" aria-label=\"Interactive chart: Cross-entropy training and selection histories for the fixed three-class classifier\"><\/div>\n<p><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/pancreatic-cancer-training-history-2026.png\" alt=\"Cross-entropy training and selection histories for the fixed three-class classifier\"><script type=\"application\/json\" id=\"nd-3513-03-data\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],\"y\":[1.1699999570846558,0.8460000157356262,0.7910000085830688,0.7390000224113464,0.7099999785423279,0.6919999718666077,0.6819999814033508,0.6729999780654907,0.6679999828338623,0.6660000085830688,0.6650000214576721,0.6629999876022339,0.6620000004768372,0.6610000133514404,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437,0.6600000262260437]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Selection error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],\"y\":[0.8550000190734863,0.8259999752044678,0.7919999957084656,0.7820000052452087,0.7720000147819519,0.7649999856948853,0.7540000081062317,0.7429999709129333,0.7390000224113464,0.7369999885559082,0.7360000014305115,0.7360000014305115,0.7360000014305115,0.7360000014305115,0.7350000143051147,0.734000027179718,0.7329999804496765,0.7329999804496765,0.7329999804496765,0.7329999804496765,0.734000027179718,0.734000027179718,0.7350000143051147,0.7350000143051147,0.7350000143051147,0.734000027179718,0.734000027179718,0.734000027179718,0.734000027179718,0.734000027179718,0.734000027179718,0.734000027179718,0.734000027179718,0.734000027179718,0.7329999804496765]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Quasi-Newton method error history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,34],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.2002000000000002],\"title\":{\"text\":\"CrossEntropy\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>The selection curve is used to monitor optimization. It does not turn the internal random split into external clinical validation.<\/figcaption><\/figure>\n<\/section>\n<section id=\"5-selection\" class=\"nds-card\">\n<h2>5. Model selection and baseline<\/h2>\n<p><strong>No neuron selection, input selection or architecture-selection experiment was performed.<\/strong> The direct 12\u20133 softmax classifier is both the initial and final model. This restraint is appropriate: adding hidden neurons would not solve centre effects, missing-data bias or the lack of an external cohort.<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Reference<\/th>\n<th>Testing accuracy<\/th>\n<th>Interpretation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Majority-class baseline<\/th>\n<td>39.0%<\/td>\n<td>Always predict benign hepatobiliary disease (46 of 118)<\/td>\n<\/tr>\n<tr>\n<th>Fixed categorical model<\/th>\n<td>66.9%<\/td>\n<td>79 of 118 testing records classified correctly<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/section>\n<section id=\"6-clinical-validation\" class=\"nds-card\">\n<h2>6. Clinical validation<\/h2>\n<p>The held-out testing subset contains 46 benign-disease, 40 no-disease and 32 PDAC records. Its class prevalence is therefore 39.0%, 33.9% and 27.1%, respectively. Predictions use the largest of the three scores (an argmax decision rule), not a binary threshold.<\/p>\n<p>A multiclass ROC AUC or precision\u2013recall analysis was not generated for this experiment, so none is claimed. The confusion matrix and per-class metrics provide the auditable evidence available from the exported project.<\/p>\n<h3>Testing confusion matrix<\/h3>\n<div class=\"nds-validation-grid\">\n<div class=\"nds-table-scroll\">\n<table class=\"nds-confusion\">\n<thead>\n<tr>\n<th>Actual \/ predicted<\/th>\n<th>Benign disease<\/th>\n<th>No disease<\/th>\n<th>PDAC<\/th>\n<th>Total<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Benign disease<\/th>\n<td>28<\/td>\n<td>12<\/td>\n<td>6<\/td>\n<td>46<\/td>\n<\/tr>\n<tr>\n<th>No disease<\/th>\n<td>14<\/td>\n<td>23<\/td>\n<td>3<\/td>\n<td>40<\/td>\n<\/tr>\n<tr>\n<th>PDAC<\/th>\n<td>3<\/td>\n<td>1<\/td>\n<td>28<\/td>\n<td>32<\/td>\n<\/tr>\n<tr>\n<th>Total<\/th>\n<td>45<\/td>\n<td>36<\/td>\n<td>37<\/td>\n<td>118<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-table-scroll\">\n<table class=\"nds-metrics\">\n<thead>\n<tr>\n<th>Class<\/th>\n<th>Precision \/ PPV<\/th>\n<th>Recall \/ sensitivity<\/th>\n<th>Specificity<\/th>\n<th>F1<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Benign disease<\/th>\n<td>62.2%<\/td>\n<td>60.9%<\/td>\n<td>76.4%<\/td>\n<td>0.615<\/td>\n<\/tr>\n<tr>\n<th>No disease<\/th>\n<td>63.9%<\/td>\n<td>57.5%<\/td>\n<td>83.3%<\/td>\n<td>0.605<\/td>\n<\/tr>\n<tr>\n<th>PDAC<\/th>\n<td>75.7%<\/td>\n<td>87.5%<\/td>\n<td>89.5%<\/td>\n<td>0.812<\/td>\n<\/tr>\n<tr>\n<th>Macro average<\/th>\n<td>67.3%<\/td>\n<td>68.6%<\/td>\n<td>83.1%<\/td>\n<td>0.677<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<div class=\"nds-figure-grid\"><\/div>\n<div class=\"nds-note\"><strong>Clinical interpretation.<\/strong> PDAC is the strongest class on this internal split, with 87.5% sensitivity and 89.5% specificity, while benign disease and no disease are confused more often. These are case-control record-level results, not estimates of performance or predictive value in a screening population.<\/div>\n<\/section>\n<section id=\"7-workflow\" class=\"nds-card\">\n<h2>7. Workflow and reproducibility<\/h2>\n<p>A credible translational workflow would separate assay processing, model inference and clinical interpretation:<\/p>\n<div class=\"nds-flow\">\n<div>Eligible research specimen<\/div>\n<div>Assay and quality control<\/div>\n<div>Schema, units and missingness<\/div>\n<div>Three-class model scores<\/div>\n<div>Applicability and uncertainty checks<\/div>\n<div>Expert review and confirmation<\/div>\n<\/div>\n<p>The interactive calculation below reproduces the project. It deliberately shows provenance fields to make the deployed input contract visible; that does not make those fields appropriate predictors for a future clinical model.<\/p>\n<div id=\"nds-pancreatic-calculator\" class=\"nds-calculator\">\n<h3>Reproduce a three-class model calculation<\/h3>\n<p>The default specimen is the reference vector exported from Neural Designer. The calculation runs locally with the exact preprocessing and trained coefficients.<\/p>\n<div class=\"nds-note\"><strong>Research demonstration.<\/strong> Cohort and sample origin are included only because they are inputs in the published project; they are provenance fields, not biomarkers. Values outside the validated domain are rejected. The output must not be used for screening, diagnosis or treatment and must not guide care.<\/div>\n<div class=\"nds-calculator-grid\">\n<label>Patient cohort<select name=\"cohort\" data-default=\"Cohort1\"><option>Cohort1<\/option><option>Cohort2<\/option><\/select><\/label><br \/>\n<label>Sample origin<select name=\"origin\" data-default=\"LIV\"><option>BPTB<\/option><option>ESP<\/option><option selected>LIV<\/option><option>UCL<\/option><\/select><\/label><br \/>\n<label>Age (years)<input name=\"age\" type=\"number\" min=\"26\" max=\"89\" step=\"1\" value=\"47\" data-default=\"47\"><\/label><br \/>\n<label>Sex<select name=\"sex\" data-default=\"M\"><option>F<\/option><option selected>M<\/option><\/select><\/label><br \/>\n<label>Plasma CA19-9<input name=\"ca19\" type=\"number\" min=\"0\" max=\"31000\" step=\"any\" value=\"21\" data-default=\"21\"><\/label><br \/>\n<label>Creatinine<input name=\"creatinine\" type=\"number\" min=\"0.05655\" max=\"4.11684\" step=\"any\" value=\"2.02\" data-default=\"2.02\"><\/label><br \/>\n<label>LYVE1<input name=\"lyve1\" type=\"number\" min=\"0.00012943\" max=\"23.890323\" step=\"any\" value=\"5.57\" data-default=\"5.57\"><\/label><br \/>\n<label>REG1B<input name=\"reg1b\" type=\"number\" min=\"0.001104422\" max=\"1403.8976\" step=\"any\" value=\"28\" data-default=\"28\"><\/label><br \/>\n<label>TFF1<input name=\"tff1\" type=\"number\" min=\"0.00529308\" max=\"13344.3\" step=\"any\" value=\"982\" data-default=\"982\"><\/label>\n<\/div>\n<div class=\"nds-calculator-actions\"><button type=\"button\" data-action=\"calculate\">Calculate three scores<\/button><button type=\"button\" class=\"nds-secondary-action\" data-action=\"reset\">Reset example<\/button><\/div>\n<div class=\"nds-calculator-error\" role=\"alert\" aria-live=\"assertive\"><\/div>\n<div class=\"nds-health-score nds-multiclass-score\" aria-live=\"polite\"><\/div>\n<\/div>\n<p> <script data-noptimize=\"1\">(() => { const root=document.getElementById('nds-pancreatic-calculator'); if(!root||root.dataset.ready==='1')return; root.dataset.ready='1'; const c={\"multipliers\":[2,2,2,2,2,0.07634520531,2,0.0005349967978,1.566205025,0.2910462618,0.005099412985,0.0009904716862],\"offsets\":[-1,-1,-1,-1,-1,-4.510451794,-1,-0.3498895168,-1.33970511,-0.891628921,-0.5699818134,-0.5921723247],\"bias\":[0.6008005142,-0.6053707004,0.004570025019],\"weights\":[[0.5179594755,-0.8087168336,-0.2735654712,-0.3672496974,0.2780142128,-0.3646245897,0.0872150436,-0.480456382,-0.02248272486,0.03055324592,-0.06768345088,0.2437823713],[-0.04628950357,0.8682414889,0.3530108631,-0.3179728389,0.1848067641,-0.04167422652,-0.1347419173,-0.1483472884,0.2830753922,-0.6705928445,-0.2784170508,-0.4416207075],[-0.4397063553,-0.02083783783,-0.05549258739,0.6361019611,-0.5064793229,0.5362083316,0.1005195603,0.7345330119,-0.2558872104,0.6755434871,0.2624566257,0.2129193991]],\"classes\":[\"Benign hepatobiliary disease\",\"No pancreatic disease\",\"Pancreatic ductal adenocarcinoma\"]}; const q=s=>root.querySelector(s); const error=q('.nds-calculator-error'); const result=q('.nds-health-score'); const numeric=[['age',26,89],['ca19',0,31000],['creatinine',0.05655,4.11684],['lyve1',0.00012943,23.890323],['reg1b',0.001104422,1403.8976],['tff1',0.00529308,13344.3]]; const render=scores=>{ const labels=c.classes.map((name,i)=>{const row=document.createElement('div');const label=document.createElement('span');const value=document.createElement('strong');label.textContent=name;value.textContent=(scores[i]*100).toFixed(2)+'% model score';row.append(label,value);return row;}); const note=document.createElement('p');note.textContent='Scores sum to 100%, but they have not been independently calibrated as clinical probabilities.'; result.replaceChildren(...labels,note); }; const calculate=()=>{ try{error.textContent=''; const vals={}; numeric.forEach(([name,min,max])=>{const el=q('[name=\"'+name+'\"]');el.classList.remove('is-invalid');const value=Number(el.value);if(!Number.isFinite(value)||value<min||value>max){el.classList.add('is-invalid');throw new Error('Enter complete values inside the ranges represented in the dataset.');}vals[name]=value;}); const cohort=q('[name=\"cohort\"]').value==='Cohort2'?1:0; const origin=q('[name=\"origin\"]').value; const sex=q('[name=\"sex\"]').value==='M'?1:0; const input=[cohort,origin==='BPTB'?1:0,origin==='ESP'?1:0,origin==='LIV'?1:0,origin==='UCL'?1:0,vals.age,sex,vals.ca19,vals.creatinine,vals.lyve1,vals.reg1b,vals.tff1]; const scaled=input.map((v,i)=>v*c.multipliers[i]+c.offsets[i]); const logits=c.bias.map((b,k)=>b+c.weights[k].reduce((sum,w,i)=>sum+w*scaled[i],0)); const max=Math.max(...logits);const exp=logits.map(x=>Math.exp(x-max));const total=exp.reduce((a,b)=>a+b,0);render(exp.map(x=>x\/total));}catch(exception){error.textContent=exception.message;result.replaceChildren();}}; q('[data-action=\"calculate\"]').addEventListener('click',calculate);q('[data-action=\"reset\"]').addEventListener('click',()=>{root.querySelectorAll('input,select').forEach(el=>{el.value=el.dataset.default;el.classList.remove('is-invalid');});calculate();});calculate(); })();<\/script><\/p>\n<h3>Reproduce the project<\/h3>\n<p>The Python package contains the exact 12-input export, schema and reference case. The Neural Designer package preserves the project, split and regenerated analyses. The CSV is the 590-row source table used by this example.<\/p>\n<pre><code>from model import NeuralNetwork\ninputs = [0, 0, 0, 1, 0, 47, 1, 21, 2.02, 5.57, 28, 982]\nscores = NeuralNetwork().calculate_outputs(inputs)<\/code><\/pre>\n<div class=\"nds-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/pancreatic-cancer-python-model-2026.zip\">Download Python model (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/pancreatic-cancer-neural-designer-project-2026.zip\">Download Neural Designer project (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/pancreatic-cancer.csv\">Download pancreatic-cancer.csv<\/a><\/div>\n<\/section>\n<section id=\"8-safety\" class=\"nds-card\">\n<h2>8. Safety, generalizability and governance<\/h2>\n<ul>\n<li><strong>Case-control design.<\/strong> The sample deliberately contains disease groups and does not represent screening prevalence, referral pathways or consecutive clinical practice.<\/li>\n<li><strong>Internal random split only.<\/strong> There is no external, temporal, prospective or site-held-out validation.<\/li>\n<li><strong>Centre proxies.<\/strong> Cohort and sample origin may improve internal discrimination by encoding collection or laboratory differences rather than disease biology.<\/li>\n<li><strong>Missing-data risk.<\/strong> CA19-9 is absent in 40.7% of rows. The stored mean is calculated from the complete project data; production-grade evaluation must fit imputation on training data only and examine informative missingness.<\/li>\n<li><strong>Uncalibrated scores.<\/strong> Softmax outputs are ranking scores until multiclass calibration is assessed on independent data.<\/li>\n<li><strong>Assay portability.<\/strong> Units, pre-analytics, instruments, batches and laboratory quality controls must match the validated specification.<\/li>\n<li><strong>Human expert review.<\/strong> Model scores cannot replace specialist interpretation, confirmatory testing or a documented clinical pathway.<\/li>\n<li><strong>No early-stage claim.<\/strong> Stage is excluded as a predictor, and this analysis does not establish sensitivity for stage I\u2013II disease.<\/li>\n<\/ul>\n<div class=\"nds-note nds-note--warning\"><strong>Decision boundary.<\/strong> Use the example for reproducible research and education only. Consequential interpretation requires a locked biomarker specification, training-only preprocessing, calibration, site-held-out and external prospective validation, and appropriate clinical confirmation.<\/div>\n<\/section>\n<section id=\"references\" class=\"nds-card\">\n<h2>References<\/h2>\n<ul>\n<li>Debernardi S, et al. <a href=\"https:\/\/journals.plos.org\/plosmedicine\/article?id=10.1371%2Fjournal.pmed.1003489\">A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: a case\u2013control study<\/a>. <em>PLOS Medicine<\/em>. 2020.<\/li>\n<li><a href=\"https:\/\/www.kaggle.com\/datasets\/johnjdavisiv\/urinary-biomarkers-for-pancreatic-cancer\">Urinary biomarkers for pancreatic cancer data collection<\/a>.<\/li>\n<\/ul>\n<\/section>\n<\/div>\n<\/div>\n<p><script data-nd-chart-migration=\"1\" data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-nd-chart-migration=\"1\" data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script><\/p>\n","protected":false},"author":11,"featured_media":1759,"template":"","categories":[29],"tags":[38],"class_list":["post-3513","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>Pancreatic cancer prediction with machine learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to detect pancreatic cancer using urinary biomarkers with different cancer risk factors.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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