CT Texture Analysis of Ductal Adenocarcinoma Downstaged After Chemotherapy

Ciaravino, Valentina; Cardobi, Nicolò; Robertis, Riccardo De; Capelli, Paola; Melisi, Davide; Simionato, Francesca; Marchegiani, Giovanni; Salvia, Roberto; D’Onofrio, Mirko

doi:10.21873/anticanres.12803

Cited by 35 publications

(27 citation statements)

References 24 publications

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“…The results showed that kurtosis differed significantly before and after neoadjuvant therapy in the downstaged group. No significant changes between texture features were found in the disease progression group [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

“…Studies with missing data on best predictive performance (accuracy or area under the curve) were excluded from these plots. No feature reduction or adjustment for multiple testing was performed in 14/60 (23%) of studies, which may have resulted in overfitting of models [ 22 – 24 , 37 , 39 – 44 , 67 , 73 – 75 ]. Study populations were relatively small compared to the number of parameters analyzed.…”

Section: Resultsmentioning

confidence: 99%

“…In total, 4/60 (6.7%) included studies focused on patients with pancreatic cancer [74][75][76][77]. In the majority of the studies, radiomic features were extracted from CT imaging.…”

Section: Pancreatic Cancermentioning

confidence: 99%

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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Wesdorp

Hellingman

Jansma

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Wesdorp

Hellingman

Jansma

et al. 2020

Eur J Nucl Med Mol Imaging

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“…Thus, it can be used to formulate the optimal treatment plan for PDAC patients. Ciaravino et al [ 61 ] investigated the added value of CT texture analysis in estimating tissue changes in PDAC downsized and resected after chemotherapy. The results suggested that the change of kurtosis before and after treatment showed a statistically significant difference, suggesting that CT texture analysis can assess tumor heterogeneity, tissue changes, and tumor downstaging in PDAC cases with no significant alteration in tumor size after NAT.…”

Section: Response Assessment Of Pdac After Natmentioning

confidence: 99%

Role of imaging in evaluating the response after neoadjuvant treatment for pancreatic ductal adenocarcinoma

Zhang¹,

Huang²,

Song³

2021

WJG

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Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Despite the development of multimodality treatments, including surgical resection, radiotherapy, and chemotherapy, the long-term prognosis of patients with PDAC remains poor. Recently, the introduction of neoadjuvant treatment (NAT) has made more patients amenable to surgery, increasing the possibility of R0 resection, treatment of occult micro-metastasis, and prolongation of overall survival. Imaging plays a vital role in tumor response evaluation after NAT. However, conventional imaging modalities such as multidetector computed tomography have limited roles in the assessment of tumor resectability after NAT for PDAC because of the similar appearance of tissue fibrosis and tumor infiltration. Perfusion computed tomography, using blood perfusion as a biomarker, provides added value in predicting the histopathologic response of PDAC to NAT by reflecting the changes in tumor matrix and fibrosis content. Other imaging technologies, including diffusion-weighted imaging of magnetic resonance imaging and positron emission tomography, can reveal the tumor response by monitoring the structural changes in tumor cells and functional metabolic changes in tumors after NAT. In addition, with the renewed interest in data acquisition and analysis, texture analysis and radiomics have shown potential for the early evaluation of the response to NAT, thus improving patient stratification to achieve accurate and intensive treatment. In this review, we briefly introduce the application and value of NAT in resectable and unresectable PDAC. We also summarize the role of imaging in evaluating the response to NAT for PDAC, as well as the advantages, limitations, and future development directions of current imaging techniques.

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“…Some of these parameters fall within the general concept of Radiomics, i.e., the analysis of medical images aimed at obtaining quantitative information that cannot be detected through their simple visual observation [8,9]. Radiomics has been applied with excellent results to various radiological fields, mainly in oncology, often reaching a significant prognostic value [10][11][12][13]. It has been used in the distinction between inflammatory/infectious and oncological disease [14], in the discrimination between primary tuberculous pneumonia and acquired pneumonia in children [15] and in the prediction of the onset of immunotherapy-induced pneumonia [16].…”

Section: Introductionmentioning

confidence: 99%

CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias

et al. 2021

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Purpose To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. Material and Methods CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C−), respectively. C− patients, however, presented with interstitial lung involvement. A subgroup of C−, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. Results The first model classified C + and C− pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C− (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). Conclusion Whole lung ML models based on radiomics can classify C + and C− interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.

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CT Texture Analysis of Ductal Adenocarcinoma Downstaged After Chemotherapy

Abstract: This evaluation may represent an added value in tumor tissue changes judgment and can be extremely useful to diagnose downstaging in those cases with no evident downsizing after chemotherapy.

Cited by 35 publications

References 24 publications

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Role of imaging in evaluating the response after neoadjuvant treatment for pancreatic ductal adenocarcinoma

CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias

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