Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort

Ishaan Cherukuri

q-bio.NCMar 27, 2026

Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort

Authors: Ishaan Cherukuri

Organizations: Independent Researcher, USA

Abstract

Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration. This uncertainty limits enrollment in clinical trials and delays urgent treatment. The Boundary Sharpness Coefficient (BSC) measures how well-defined the gray-white matter boundary looks on structural MRI. This study measures how BSC changes over time, namely, how fast the boundary degrades each year works much better than looking at a single baseline scan for predicting MCI-to-AD conversion. This study analyzed 1,824 T1-weighted MRI scans from 450 ADNI subjects (95 converters, 355 stable; mean follow-up: 4.84 years). BSC voxel-wise maps were computed using tissue segmentation at the gray-white matter cortical ribbon. Previous studies have used CNN and RNN models that reached 96.0% accuracy for AD classification and 84.2% for MCI conversion, but those approaches disregard specific regions within the brain. This study focused specifically on the gray-white matter interface. The approach uses temporal slope features capturing boundary degradation rates, feeding them into Random Survival Forest, a non-parametric ensemble method for right-censored survival data. The Random Survival Forest trained on BSC slopes achieved a test C-index of 0.63, a 163% improvement over baseline parametric models (test C-index: 0.24). Structural MRI costs a fraction of PET imaging ( $800--$ 1,500 vs. $5,000--$ 7,000) and does not require CSF collection. These temporal biomarkers could help with patient-centered safety screening as well as risk assessment.

Figures & tables

Figure 1: Complete BSC slope-based MCI-to-AD conversion prediction pipeline. Longitudinal T1-weighted MRI scans from four time points (baseline, m12, m24, m36) undergo preprocessing (N4 bias correction, skull stripping, resampling), Atropos segmentation to derive GM/WM probability maps, and BSC computation at the gray-white matter boundary. Per-scan features (35 total) are extracted from each time point, then organized into per-subject longitudinal sequences. Linear regression across time points yields 182 slope-based features per subject capturing annual rates of boundary degradation. The top 20 most variable slopes are selected and input to a Random Survival Forest model, which outputs individual risk scores, Kaplan-Meier survival curves stratified by risk group, and performance metrics (C-index: 0.63).

Feature Category	Count
Boundary count (Nboundary)	1
Directional statistics (mean, std, median)	3
Directional percentiles (p10, p25, p50, p75, p90)	5
Magnitude statistics (mean, std, median)	3
Magnitude percentiles (p10, p25, p50, p75, p90)	5
Directional spatial bins (8 bins)	8

Table 1: BSC feature categories extracted per scan

Figure 2: Random Survival Forest algorithm schematic. The original training data is repeatedly bootstrap sampled (typically 63% of subjects with replacement, leaving 37% as out-of-bag samples). For each bootstrap sample, a survival tree is grown by recursively splitting nodes using randomly selected candidate variables that maximize survival differences between daughter branches (measured by log-rank statistics). This process generates an ensemble of diverse trees (Ntree = 1,000 in this implementation). For prediction, each tree gives a cumulative hazard estimate based on which terminal node the subject falls into, and the final ensemble prediction averages across all trees. The out-of-bag (OOB) data gives internal validation for calculating error rates and variable importance without needing a separate validation set.

Characteristic	All	Converters	Stable
	(N=450)	(N=95)	(N=355)
Age (years), mean±SD	74.4±5.9	75.8±5.7	73.8±5.8
Female, n (%)	225 (50.0)	42 (44.2)	183 (51.5)
Baseline Nboundary	17,353±5,891	13,978±5,634	18,733±5,507
Follow-up (years)	4.84±3.79	1.90±1.39	5.63±3.84

Table 2: Demographic and clinical characteristics of the study cohort

Model	Features	C-index (Train)	C-index (Test)	RMSE (years)	N Features
Weibull AFT	Baseline BSC (all)	0.34	0.24	11.80	8
Weibull AFT	Slopes (top 20)	0.34	0.61	34.56	20
Random Survival Forest	Slopes (top 20)	0.84	0.63	13.05	20

Table 3: Survival model performance comparison: Baseline BSC vs. Longitudinal Slopes

Figure 3: Cross-validation performance comparison across 8 model variants. Box plots show C-index distributions from 5-fold cross-validation for training (left), test (middle), and overfitting gap (right) sets. RSF models with different feature counts (10, 15, 20, 25, 30) and parametric AFT models (Weibull, LogNormal, LogLogistic) all using top-variance slope features. RSF-20 hits the best balance: median training C-index of 0.82, test of 0.63, though substantial overfitting remains (gap: 0.19). Parametric models show more stable train-test gaps but lower overall performance.

Figure 4: Kaplan-Meier survival curves stratified by Random Survival Forest predicted risk scores. Training set (left) and test set (right) show probability of remaining MCI (not converting to AD) over time for three risk groups defined by risk score tertiles. High-risk subjects (red, top tertile) show rapid conversion with median survival 2.1 years. Medium-risk subjects (orange) exhibit intermediate progression rates. Low-risk subjects (green, bottom tertile) show slow conversion with median survival 8.5 years. Shaded regions indicate 95% confidence intervals. Log-rank test confirms significant separation between groups (p=0.3245 for test set, high vs. low comparison). Event counts per group shown in legend demonstrate adequate statistical power for hazard estimation.

Feature	Variance
Nboundary_slope	0.142
bsc_mag_p90_slope	0.024
bsc_mag_p75_slope	0.022
bsc_dir_p90_slope	0.020
bsc_dir_p75_slope	0.021
bsc_mag_p50_slope	0.024

Table 4: Top 10 BSC slope features selected by penalized robust variance

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Calibrated Alzheimer's Conversion Risk in Mild Cognitive Impairment: Persistent Homology of Clinical Trajectories with Conformal Guarantees

Navin Bondade

Institute of Health Informatics, University College London

Clinical Outcome Prediction Alzheimer's Disease Classification Persistent Homology Alzheimer's Disease Progression Prediction

Open paper AskPDF arXiv

Background. Predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is central to trial enrichment and care planning, yet existing models provide no individual-level uncertainty estimates and rarely include transparent leakage audits. We introduce the first application of persistent homology to longitudinal clinical trajectory point clouds for this task, and the first split-conformal individual risk guarantee for any AD-conversion model. Methods. We analysed 741 MCI subjects (240 converters, 32.4%) from ADNI with a uniform 4-year follow-up cap. Five leakage sources were corrected; without them a naive pipeline achieved AUC=0.934, inflated by +0.075. Vietoris-Rips persistent homology and sublevel-set proxies were combined with trajectory slopes and engineered features (76 total) in a stacking ensemble evaluated by 5-fold cross-validation. Results. Cox and Random Survival Forest models with TDA features achieved concordance C=0.799 and C=0.826 versus C=0.753 and C=0.812 without (+0.045 and +0.014). The primary nested AUC is 0.840 (same-fold bound 0.866); external AUC was 0.879 on a zero-overlap ADNI-2/GO/3 cohort. H0 persistence entropy was the top SHAP feature and significantly associated with APOE4 dosage (Spearman r=-0.191, p<0.0001, Bonferroni-corrected). Cross-conformal coverage was 90.4%+-2.2% (target 90%); empirical external coverage 96.9%. Maximum fairness gap in false-negative rate across seven subgroups was 0.092. Conclusions. We propose H0 persistence entropy as a topological biomarker of cognitive decline and demonstrate that a leakage-audited, conformally calibrated pipeline reaches competitive accuracy with individual-level uncertainty quantification not previously available for this task.

Abstract

Alzheimer's Disease Progression Prediction

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