# Alhadi Almangush

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/alhadi-almangush/

## Facts

| Field | Value |
| --- | --- |
| Citations | 4,022 |
| Field | Head and Neck Cancer Studies |
| h-index | 38 |
| i10-index | 62 |
| Last Known Institution | University of Helsinki |
| OpenAlex ID | https://openalex.org/A5042870457 |
| ORCID iD | 0000-0003-4106-314X |
| Works | 118 |

## Researcher papers

- [Depth of invasion, tumor budding, and worst pattern of invasion: Prognostic indicators in early‐stage oral tongue cancer](https://scholariq.org/papers/depth-of-invasion-tumor-budding-and-worst-pattern-of-invasion-prognostic/)
- [Prognostic biomarkers for oral tongue squamous cell carcinoma: a systematic review and meta-analysis](https://scholariq.org/papers/prognostic-biomarkers-for-oral-tongue-squamous-cell-carcinoma-a-systematic/)
- [Tumour budding in oral squamous cell carcinoma: a meta-analysis](https://scholariq.org/papers/tumour-budding-in-oral-squamous-cell-carcinoma-a-meta-analysis/)
- [Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review](https://scholariq.org/papers/machine-learning-in-oral-squamous-cell-carcinoma-current-status-clinical/)
- [Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP](https://scholariq.org/papers/machine-learning-explainability-in-nasopharyngeal-cancer-survival-using-lime-and/)
- [For early-stage oral tongue cancer, depth of invasion and worst pattern of invasion are the strongest pathological predictors for locoregional recurrence and mortality](https://scholariq.org/papers/for-early-stage-oral-tongue-cancer-depth-of-invasion-and-worst-pattern-of/)
- [Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer](https://scholariq.org/papers/comparison-of-supervised-machine-learning-classification-techniques-in/)
- [A simple novel prognostic model for early stage oral tongue cancer](https://scholariq.org/papers/a-simple-novel-prognostic-model-for-early-stage-oral-tongue-cancer/)
- [The Impact of Histopathological Features on the Prognosis of Oral Squamous Cell Carcinoma: A Comprehensive Review and Meta-Analysis](https://scholariq.org/papers/the-impact-of-histopathological-features-on-the-prognosis-of-oral-squamous-cell/)
- [Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool](https://scholariq.org/papers/machine-learning-application-for-prediction-of-locoregional-recurrences-in-early/)
- [Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication](https://scholariq.org/papers/measuring-the-usability-and-quality-of-explanations-of-a-machine-learning-web/)

## Researcher topics

- [Head and Neck Cancer Studies](https://scholariq.org/topics/head-and-neck-cancer-studies/)
- [Oral Health Pathology and Treatment](https://scholariq.org/topics/oral-health-pathology-and-treatment/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [Cancer-related molecular mechanisms research](https://scholariq.org/topics/cancer-related-molecular-mechanisms-research/)
- [Cancer Immunotherapy and Biomarkers](https://scholariq.org/topics/cancer-immunotherapy-and-biomarkers/)

## Researcher university

- [University of Helsinki](https://scholariq.org/institutions/university-of-helsinki/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.