CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases

Taghavi, Marjaneh; Staal, Femke C.R.; Muñoz, F.; Imani, Farshad; Meek, David B.; Simões, Rita; Klompenhouwer, Lisa G.; Heide, Uulke A. van der; Maas, Monique

doi:10.1007/s00270-020-02735-8

Cited by 30 publications

(22 citation statements)

References 32 publications

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“…AI application in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns[ 41 , 47 ] could give a significant contribution towards a rapid oncological individualized approach and treatments. AI technologies have also shown potential as a prognostic and outcome tool, predicting with good accuracy response to chemotherapy[ 54 , 55 , 57 , 58 ], early local tumor progression after ablation treatment[ 59 ], and patient survival after surgery or chemotherapy[ 60 , 64 - 66 ].…”

Section: Discussionmentioning

confidence: 99%

“…In order to predict early local tumor progression after ablation treatment of up to five nodules per patient with a maximum diameter of 30 mm, Taghavi et al [ 59 ] developed a ML-based radiomics analysis of the pretreatment CT scan combined with patients’ clinical features that showed a concordance index in the validation cohort of 0.79 (95%CI: 0.78-0.80).…”

Section: Ai Models For Treated Crlmmentioning

confidence: 99%

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Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Rompianesi¹,

Pegoraro²,

Ceresa³

et al. 2022

WJG

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Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients’ survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients’ exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies’ role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.

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Section: Discussionmentioning

confidence: 99%

Section: Ai Models For Treated Crlmmentioning

confidence: 99%

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Rompianesi¹,

Pegoraro²,

Ceresa³

et al. 2022

WJG

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“…The comparative analysis of the imaging modalities before and after chemotherapy further refined the prediction of the long-term outcome[ 89 , 91 , 92 , 94 ], and there is accumulating evidence that both radiomic scores and combined clinical-radiomic models outperform traditional predictors of survival[ 92 ]. Third, textural features of the tumor before thermal ablation can predict the risk of local recurrence[ 97 ]. Fourth, radiomics are associated with the pathology data ( e.g.…”

Section: Radiomics: Imaging Beyond the Visible Datamentioning

confidence: 99%

Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

Viganò¹,

Arachchige²,

Fiz³

2022

WJG

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The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.

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“…Furthermore, radiomic signatures were significantly associated with overall survival in both studies. In another study, a CT-based radiomics model outperformed a clinical model to detect local tumor progression in 31 lesions after thermal ablation [ 113 ]. In addition to predicting the therapy response, radiomics may improve the diagnostic accuracy of chemotherapy-associated liver injury (CALI).…”

Section: Radiomicsmentioning

confidence: 99%

The Role of Biomarkers in the Management of Colorectal Liver Metastases

Hewitt

Brown

Pawlik

2022

Cancers

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Surgical management combined with improved systemic therapies have extended 5-year overall survival beyond 50% among patients with colorectal liver metastases (CRLM). Furthermore, a multitude of liver-directed therapies has improved local disease control for patients with unresectable CRLM. Unfortunately, a significant portion of patients treated with curative-intent hepatectomy develops disease recurrence. Traditional markers fail to risk-stratify and prognosticate patients with CRLM appropriately. Over the last few decades, advances in molecular sequencing technology have greatly expanded our knowledge of the pathophysiology and tumor microenvironment characteristics of CRLM. These investigations have revealed biomarkers with the potential to better inform management decisions in patients with CRLM. Actionable biomarkers such as RAS and BRAF mutations, microsatellite instability/mismatch repair status, and tumor mutational burden have been incorporated into national and societal guidelines. Other biomarkers, including circulating tumor DNA and radiomic features, are under active investigation to evaluate their clinical utility. Given the plethora of therapeutic modalities and lack of evidence on timing and sequence, reliable biomarkers are needed to assist clinicians with the development of patient-tailored management plans. In this review, we discuss the current evidence regarding biomarkers for patients with CRLM.

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CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases

Cited by 30 publications

References 32 publications

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

The Role of Biomarkers in the Management of Colorectal Liver Metastases

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