Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

Bodalal, Zuhir; Boellaard, Thierry N.; Bucho, Teresa M. Tareco; Drago, Silvia Girolama; Kurilova, Ieva; Calin-Vainak, Adriana M.; Pizzi, Andrea Delli; Muller, Mirte; Hummelink, Karlijn; Hartemink, Koen J.; Nguyen-Kim, Thi Dan Linh; Smit, Egbert F.; Aerts, Hugo J.W.L.

doi:10.3389/fonc.2021.609054

Cited by 30 publications

(40 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In-depth analysis revealed stark differences in the prognostic value of morphological changes depending on the time point of treatment, with the first 9 months of treatment being the most predictive and significant AUCs > 0.70, peaking to over 0.80 for both abdominal imaging, and thoracic imaging. Similar findings were observed in our previous study on NSCLC (8), where the changes recorded by the algorithm in the first 3 to 5 months of treatment were observed to have a higher prognostic value. In the present study, we extended the system to include both the thorax and abdomen, and trained with far larger datasets both in terms of pre-training as well as survival association.…”

Section: Discussionsupporting

confidence: 91%

“…The tracker module was designed following the research of Balakrishnan et al (11) and Zhao et al (12), as well as our previous work on chest imaging in NSCLC (8). The network receives two images as input (i.e., a moving and fixed one) concatenated along the channel axis.…”

Section: Tracker Modulementioning

confidence: 99%

“…Our goal was for the AI to learn to match corresponding imaging landmarks, and to cover a large set of possible variations while, simultaneously, containing its scope (or prior knowledge) to landmarks in the abdominal area. For the chest AI tracker module, we leveraged the trained weights from the NSCLC study (8). The code of both tracker and localizer have been added to the department AI repository 2 .…”

Section: Tracker Modulementioning

confidence: 99%

“…Novel techniques of computational imaging and artificial intelligence (AI) can be the basis for quantitative methods for treatment monitoring (8). Specifically, AI algorithms can be seen as methods to capture, measure, and quantify complex highly-variable anatomical phenomena for prognostic purposes, in a robust and time-efficient manner.…”

Section: Introductionmentioning

confidence: 99%

“…We term our AI system the Prognostic AI-monitor (PAM). Recently, a similar pilot approach was tested in a study on chest imaging of a NSCLC cohort (8), demonstrating accurate response prediction and a correlation with overall survival. In this study, we aim to extend the model to thoracoabdominal imaging, and validate it on a cohort of metastatic urothelial cancer patients treated with anti-PD1/PDL1.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods.Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival.Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p < 0.001) for abdominal imaging, and 0.67 AUC (p < 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p < 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p < 0.001), suggesting its complementary value in current clinical settings.Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Tracker Modulementioning

confidence: 99%

Section: Tracker Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

Non‐small cell lung cancer in China

Chen

Liu

Wen

et al. 2022

Cancer Communications

225

View full text Add to dashboard Cite

In China, lung cancer is a primary cancer type with high incidence and mortality. Risk factors for lung cancer include tobacco use, family history, radiation exposure, and the presence of chronic lung diseases. Most early‐stage non‐small cell lung cancer (NSCLC) patients miss the optimal timing for treatment due to the lack of clinical presentations. Population‐based nationwide screening programs are of significant help in increasing the early detection and survival rates of NSCLC in China. The understanding of molecular carcinogenesis and the identification of oncogenic drivers dramatically facilitate the development of targeted therapy for NSCLC, thus prolonging survival in patients with positive drivers. In the exploration of immune escape mechanisms, programmed cell death protein 1 (PD‐1)/programmed death‐ligand 1 (PD‐L1) inhibitor monotherapy and PD‐1/PD‐L1 inhibitor plus chemotherapy have become a standard of care for advanced NSCLC in China. In the Chinese Society of Clinical Oncology's guidelines for NSCLC, maintenance immunotherapy is recommended for locally advanced NSCLC after chemoradiotherapy. Adjuvant immunotherapy and neoadjuvant chemoimmunotherapy will be approved for resectable NSCLC. In this review, we summarized recent advances in NSCLC in China in terms of epidemiology, biology, molecular pathology, pathogenesis, screening, diagnosis, targeted therapy, and immunotherapy.

show abstract

Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?

Yeghaian,

Tareco Bucho,

de Bruin

et al. 2024

J Cancer Res Clin Oncol

View full text Add to dashboard Cite

Purpose In this study, we aimed to evaluate the potential of routine blood markers, serum tumour markers and their combination in predicting RECIST-defined progression in patients with stage IV non-small cell lung cancer (NSCLC) undergoing treatment with immune checkpoint inhibitors. Methods We employed time-varying statistical models and machine learning classifiers in a Monte Carlo cross-validation approach to investigate the association between RECIST-defined progression and blood markers, serum tumour markers and their combination, in a retrospective cohort of 164 patients with NSCLC. Results The performance of the routine blood markers in the prediction of progression free survival was moderate. Serum tumour markers and their combination with routine blood markers generally improved performance compared to routine blood markers alone. Elevated levels of C-reactive protein (CRP) and alkaline phosphatase (ALP) ranked as the top predictive routine blood markers, and CYFRA 21.1 was consistently among the most predictive serum tumour markers. Using these classifiers to predict overall survival yielded moderate to high performance, even when cases of death-defined progression were excluded. Performance varied across the treatment journey. Conclusion Routine blood tests, especially when combined with serum tumour markers, show moderate predictive value of RECIST-defined progression in NSCLC patients receiving immune checkpoint inhibitors. The relationship between overall survival and RECIST-defined progression may be influenced by confounding factors.

show abstract

Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

Cited by 30 publications

References 52 publications

Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy

Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy

Non‐small cell lung cancer in China

Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?

Contact Info

Product

Resources

About