Lung Cancer Diagnosis and Treatment

Latest papers

• Evaluating Multi-Task Morphological Concept Learning for Pulmonary Nodule Malignancy Assessment in 3D CT

  Namitha Narayanan · 1 October 2026

  Morphological characteristics such as spiculation and lobulation play an important role in assessing pulmonary nodules on computed tomography (CT), particularly in relation to malignancy risk. This study examines whether learning radiologist-annotated morphological features together with malignancy …

• Performance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening

  Benjamin Renoust, Pierre Baudot, Tiffany Foriel, Yousra Haddou, Charles Voyton, Pierre-Henri Siot, Ezequiel Geremia, Danny Francis, Jean-Christophe Brisset, Val\'erie Bourd\`es, Sylvain Bodard, Benoit Huet · 22 September 2026

  Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured clinical interpretation settings remains insufficiently explored. In lung cancer screening, interpretative variability persists despite standardized…

• Uncertainty-driven training for three-dimensional calibrated lung nodule classification

  Giuseppe Tripodi, Alessandro De Rosis, Saleh Rezaeiravesh · 21 September 2026

  In this work, we present an uncertainty-driven training framework for three-dimensional computed tomography (CT) lung nodule classification, where validation-based uncertainty estimates guide loss reweighting to enhance predictive performance and probability calibration. Two Uncertainty Quantificati…

• GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT

  Haojie Yang, Ran Su · 11 September 2026

  Lung cancer causes more deaths than any other malignancy, and low-dose CT screening is the main pathway to early diagnosis. That pathway hinges on the smallest lesions, yet nodules below six millimeters remain hard to detect, because most methods treat a nodule as a generic object and ignore the ima…

• Artificial Intelligence Algorithms for the Detection of Pathologies Related to Lung Cancer through Image Analysis using Convolutional Neural Networks and Data Augmentation: a systematic mapping of the literature

  Pablo Ramirez Amador · 11 September 2026

  Lung cancer is one of the leading causes of death worldwide, and its early diagnosis is crucial to improving patients prognosis and quality of life. However, the process of interpreting medical images for the detection of lung cancer is complex and requires trained experts. In this context, artifici…

• CGSM: Concept-Guided Segmentation Model for Precise Pulmonary Lesion Delineation

  Changheng Lin, Wenjie Zhang, Yushan Lu, Xinyue Yan, Xiao Jia, Wei Zhang · 10 September 2026

  Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches often lack task-specific semantic guidance, as text-based annotations typically offer coarse localization of lesions, leading to inadequate delineation …

• LeCor: Learning to Be Corrected by Meta-Learned Test-Time Training for Interactive 3D Lung-Tumour Segmentation

  Yi Luo, Yike Guo, Wenxuan Li, Zongwei Zhou, Rui Zhang, Kai Ding · 10 September 2026

  Delineating lung tumours on computed tomography (CT) takes a considerable share of the time spent on radiotherapy planning, and a contour proposed by a model can be refined interactively by the clinician. Promptable foundation models such as SAM 3 support this workflow by writing each correction int…

• Real-World Multi-Modal and Longitudinal Lung Cancer Dataset

  Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago, Catarina Barata · 7 September 2026

  Multi-modal learning has demonstrated strong potential in medical applications by integrating heterogeneous data sources such as medical imaging, clinical records, and genomics to improve predictive performance and support clinical decision-making. However, advances in this area are often constraine…

• LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology

  Marie-Lisa Eich, Kai Standvoss, Timo Milbich, Alexander M\"ollers, Miriam H\"agele, Philipp Anders, Lars Tharun, Hanna Kontradiuk, Sebastian Kons, Nader Aldoj, Recepcan Adig\"uzel, Adam Narai, Lukas H\"onig, Jonathan Striebel, Binru Yang, Mihnea P. Dragomir, Marvin Sextro, Philipp Keyl, Philipp Jurmeister, Rosemarie Krupar, Evelyn Ramberger, James Wells, Julika Ribbat-Idel, Andreas Kunft, Hussam Shuaib, Christian Groh\'e, Reinhard B\"uttner, David Horst, Klaus-Robert M\"uller, Lukas Ruff, Maximilian Alber, Frederick Klauschen, Simon Schallenberg · 26 August 2026

  Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological assessment remains largely visual and semi-quantitative and shows interobserver variability, while existi…

• Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers

  Olivera Kotevska, Ian Goethert, Michael McGee, Maria Mahbub, Sean R. Wilkinson, Rowena Yip, Myvizhi Esai Selvan, Zeynep H. Gumus, Claudia Henschke, Robert J. Klein, Providencia Morales, Samuel M Aguayo, Ioana Danciu, Mayanka Chandrashekar · 25 August 2026

  Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage malignancies can be subtle on chest X-rays, creating challenges for radiologists. This study evaluates Vision Transformers (ViTs) for predicting lun…

• Quantum Kernel Estimation for the Discovery of Early Lung Cancer Detection

  Hamed Javidi, Alex Zajichek, Hakan Doga, Laxmi Parida, Filippo Utro, Peter J. Mazzone · 21 August 2026

  Lung cancer screening with low-dose chest computed tomography reduces mortality, but its impact is limited by uptake, adherence, and management challenges. Blood-based cell-free DNA (cfDNA) biomarkers offer a complementary approach, although early detection remains difficult because of lung cancer h…

• Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT

  Anna Mrukwa, Marek Socha, Aleksandra Suwalska, Agata Durawa, Malgorzata Jelitto, Katarzyna Dziadziuszko, Edyta Szurowska, Pawel Bozek, Michal Marczyk, Witold Rzyman, Rafal Dziadziuszko, Joanna Polanska · 18 August 2026

  Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However, the growing number of patients and the limited number of radiologists lead to prolonged diagnostic waiting times. In ve…

• FZ-VLM: A Two Stage Florence-Zephyr Vision Language Model Framework for Pulmonary Nodule Characterization and Clinical Decision Making

  Pramit Dutta, Jenita Manokaran, Richa Mittal, Ryan Appleby, Eranga Ukwatta · 18 August 2026

  Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and Computed Tomography (CT) is a primary imaging tool for screening and followup assessment. After pulmonary nodule detection, radiologists manually assess anatomical location, diameter, margin characteristics, and…

• Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation

  Simon Baur, Arne Schernich, Ekin Böke, Wojciech Samek, Jackie Ma · 17 August 2026

  Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity. However, whether entropy-based measures reflect clinically meaningful ambiguity, i.e. case-leve…

• ConfTriage: A Calibration-Aware LLM Triage Framework for Pulmonary Nodule Malignancy with Selective Specialist Deferral

  Md Rabiul Islam, Samir Abdaljalil, Erchin Serpedin, Hasan Kurban · 12 August 2026

  Pulmonary nodule malignancy prediction typically depends on image-trained specialist deep learning (DL) models that require substantial annotated imaging data and task-specific training. We investigate whether a generalist large language model (LLM), reading only a faithful natural-language renderin…

• PET/CT Radiogenomic Mutation Prediction in Non-Small Cell Lung Cancer Using Multi-Label Learning

  Mona Furukawa, Sai Hyne, Daniel R. McGowan, Bart{\l}omiej W. Papie\.z · 11 August 2026

  Lung cancer remains one of the leading causes of cancer- related mortality worldwide. Although targeted therapies have improved outcomes for patients with non-small cell lung cancer (NSCLC), they rely on mutation profiling through tissue biopsy, an invasive procedure with several limitations. This s…

• Structured Proxy Features for Multimodal NSCLC Survival Prediction from Pretreatment CT

  Huu Phong Nguyen, Delower Hossain, Ehsan Saghapour, Zhandos Sembay, Jake Y. Chen · 4 August 2026

  Lung cancer results in roughly 1.8 million fatalities annually worldwide, with non-small cell lung cancer (NSCLC) comprising the majority of cases. Despite advancements in treatment, survival stratification remains challenging due to intratumoral heterogeneity inadequately captured by conventional d…

• When Does Synthetic CT Transfer? A Label-Free Donor/Host Diagnostic for Medical Vision-Language Model Routing on Real Lung CT

  Fakrul Islam Tushar · 30 June 2026

  A synthetic measurement of model competence is useful only if it survives the move to real data, yet the real labels that would verify it are exactly what medical imaging lacks. We ask whether transfer can be predicted in advance, label-free, and answer with a mechanism: on synthetic digital twins, …

• NoduLoCC2026: Lung Nodule Localization and Classification Contest from Chest X-Ray Images

  Adnan Mustafic, Halim Benhabiles, Adnane Cabani, Kristhian André Oliveira Aguilar, Romain Amigon, Clément Bardin, Chiara Bentifece, Marin Boehm, Kévin Bouchard, Laura Burattini, Diedre Carmo, Fahima Idiri, Matthis Lahargoue, Ilaria Marcantoni, Hicham Messaoudi, Cyril Meyer, Farid Meziane, Léon Morales, Letícia Rittner, Agnese Sbrollini, Léonard Zipper, Karim Hammoudi · 22 June 2026

  We propose NoduLoCC2026, a challenge on lung nodule detection and localization in chest X-ray images. We have provided a dataset for both tasks and received submissions from 5 international teams. The participating teams' solutions are presented in this work along with results on an external dataset…

• When is 3D Worth It? A Resource-Performance Frontier for CNNs and Transformers in Lung CT

  Md Enamul Hoq, Sharafat Hossain, Imraul Emmaka, Linda Larson-Prior, Lawrence Tarbox, Jonathan Bona, Donald Johann Jr. and Fred Prior · 8 June 2026

  Three-dimensional models are widely assumed preferable for volumetric medical imaging, yet their practical value depends on whether performance gains justify added computational cost and complexity. Rather than proposing a new architecture, we study how input dimensionality (2D, 2.5D, 3D) affects mo…

• Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models

  Arunkumar Kannan, Yanbo Zhang, Han Liu, Michael Baumgartner, Jianing Wang, Alexander Hertel, Bogdan Georgescu, Sasa Grbic · 1 June 2026

  While automated diagnosis systems have achieved remarkable success in computed tomography (CT)-based lung cancer screening, their development remains limited by the scarcity of diverse, annotated pulmonary nodule datasets. Diffusion-based generative models offer a promising strategy for data synthes…

• A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation

  Zhengrui Guo, Zhengyu Zhang, Jiabo Ma, Yihui Wang, Fengtao Zhou, Yingxue Xu, Ling Liang, Chenglong Zhao, Qi Xie, Jinbang Li, Shujing Guo, Fangyi Han, Zhijian Cen, Ziyi Liu, Cheng Jin, Junlin Hou, Zhixuan Chen, Yu Cai, Lijuan Qu, Shifu Chen, Yueping Liu, Zhe Wang, Xiuming Zhang, Muyan Cai, Li Liang, Hao Chen · 26 May 2026

  Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objectives. Although pan-cancer foundation models offer versatility, they lack subspecialty-level depth and have not been evalu…

• M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification

  Jinyue Li, Yuzhou Yu, Jingjing Yang, Meng Fu, Yani Zhang, Shuyao He, Dianlong Ge, Xin Ning, Yannan Chu, Qiankun Li · 13 May 2026

  The accurate classification of benign and malignant pulmonary nodules in CT scans is critical for early lung cancer screening, yet remains challenging due to the multi-scale and heterogeneous nature of pulmonary nodules. While deep learning offers potential for auxiliary diagnosis, most existing mod…

• BronchoLumen: Analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy

  Yongchao Li, Marian Himstedt · 13 May 2026

  Bronchoscopy is routinely conducted in pulmonary clinics and intensive care units, but navigating the complex branching of the respiratory tract remains challenging. This paper introduces BronchoLumen, a real-time YOLO-based system for detecting bronchial orifices in video bronchoscopy, aiming to as…

• iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models

  Fakrul Islam Tushar, Umme Hafsa Momy, Joseph Y. Lo, Geoffrey D. Rubin · 8 May 2026

  We introduce iTRIALSPACE, a programmable evaluation framework for controlled assessment of lung CT models. Standard benchmarks are static retrospective collections that entangle lesion size, lobe prevalence, anatomy, and acquisition context, making it difficult to determine what structurally drives …