Research

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15 pages, 1327 KiB

Open AccessArticle

Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer

by Carmen Herrero Vicent, Xavier Tudela, Paula Moreno Ruiz, Víctor Pedralva, Ana Jiménez Pastor, Daniel Ahicart, Silvia Rubio Novella, Isabel Meneu, Ángela Montes Albuixech, Miguel Ángel Santamaria, María Fonfria, Almudena Fuster-Matanzo, Santiago Olmos Antón and Eduardo Martínez de Dueñas

Cancers 2022, 14(14), 3508; https://doi.org/10.3390/cancers14143508 - 19 Jul 2022

Cited by 5 | Viewed by 2803

Abstract

Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, [...] Read more.

Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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13 pages, 1958 KiB

Open AccessArticle

Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer

by Divya Bhardwaj, Archya Dasgupta, Daniel DiCenzo, Stephen Brade, Kashuf Fatima, Karina Quiaoit, Maureen Trudeau, Sonal Gandhi, Andrea Eisen, Frances Wright, Nicole Look-Hong, Belinda Curpen, Lakshmanan Sannachi and Gregory J. Czarnota

Cancers 2022, 14(5), 1247; https://doi.org/10.3390/cancers14051247 - 28 Feb 2022

Cited by 8 | Viewed by 2206

Abstract

Background: This study was conducted to explore the use of quantitative ultrasound (QUS) in predicting recurrence for patients with locally advanced breast cancer (LABC) early during neoadjuvant chemotherapy (NAC). Methods: Eighty-three patients with LABC were scanned with 7 MHz ultrasound before starting NAC [...] Read more.

Background: This study was conducted to explore the use of quantitative ultrasound (QUS) in predicting recurrence for patients with locally advanced breast cancer (LABC) early during neoadjuvant chemotherapy (NAC). Methods: Eighty-three patients with LABC were scanned with 7 MHz ultrasound before starting NAC (week 0) and during treatment (week 4). Spectral parametric maps were generated corresponding to tumor volume. Twenty-four textural features (QUS-Tex¹) were determined from parametric maps acquired using grey-level co-occurrence matrices (GLCM) for each patient, which were further processed to generate 64 texture derivatives (QUS-Tex¹-Tex²), leading to a total of 95 features from each time point. Analysis was carried out on week 4 data and compared to baseline (week 0) data. ∆Week 4 data was obtained from the difference in QUS parameters, texture features (QUS-Tex¹), and texture derivatives (QUS-Tex¹-Tex²) of week 4 data and week 0 data. Patients were divided into two groups: recurrence and non-recurrence. Machine learning algorithms using k-nearest neighbor (k-NN) and support vector machines (SVMs) were used to generate radiomic models. Internal validation was undertaken using leave-one patient out cross-validation method. Results: With a median follow up of 69 months (range 7–118 months), 28 patients had disease recurrence. The k-NN classifier was the best performing algorithm at week 4 with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 75%, 81%, and 0.83, respectively. The inclusion of texture derivatives (QUS-Tex¹-Tex²) in week 4 QUS data analysis led to the improvement of the classifier performances. The AUC increased from 0.70 (0.59 to 0.79, 95% confidence interval) without texture derivatives to 0.83 (0.73 to 0.92) with texture derivatives. The most relevant features separating the two groups were higher-order texture derivatives obtained from scatterer diameter and acoustic concentration-related parametric images. Conclusions: This is the first study highlighting the utility of QUS radiomics in the prediction of recurrence during the treatment of LABC. It reflects that the ongoing treatment-related changes can predict clinical outcomes with higher accuracy as compared to pretreatment features alone. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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19 pages, 1018 KiB

Open AccessArticle

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

by Hyun-Soo Park, Kwang-sig Lee, Bo-Kyoung Seo, Eun-Sil Kim, Kyu-Ran Cho, Ok-Hee Woo, Sung-Eun Song, Ji-Young Lee and Jaehyung Cha

Cancers 2021, 13(23), 6013; https://doi.org/10.3390/cancers13236013 - 29 Nov 2021

Cited by 11 | Viewed by 2221

Abstract

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered [...] Read more.

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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11 pages, 24363 KiB

Open AccessArticle

Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images

by Chia-Hao Liang, Yung-Chi Liu, Yung-Liang Wan, Chun-Ho Yun, Wen-Jui Wu, Rafael López-González and Wei-Ming Huang

Cancers 2021, 13(22), 5600; https://doi.org/10.3390/cancers13225600 - 09 Nov 2021

Cited by 10 | Viewed by 2508

Abstract

Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively [...] Read more.

Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively enrolled subjects who suffered from IPF in this study. Clinical data including age, gender, smoking status, and pulmonary function were recorded. Non-contrast chest CT for fibrotic score calculation and determination of three dimensional measures of whole-lung texture and emphysema were performed using a promising deep learning imaging platform. The results revealed that among 116 subjects with IPF (90 non-cancer and 26 lung cancer cases), the radiomics features showed significant differences between non-cancer and cancer patients. In the training cohort, the diagnostic accuracy using selected radiomics features with AUC of 0.66–0.73 (sensitivity of 80.0–85.0% and specificity of 54.2–59.7%) was not inferior to that obtained using traditional risk factors, such as gender, smoking status, and emphysema (%). In the validation cohort, the combination of radiomics features and traditional risk factors produced a diagnostic accuracy of 0.87 AUC and an accuracy of 75.0%. In this study, we found that whole-lung CT texture analysis is a promising tool for LC risk stratification of IPF patients. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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15 pages, 2522 KiB

Open AccessArticle

A Pilot Study to Evaluate Early Predictive Value of Thorax Perfusion-CT in Advanced NSCLC

by Francisco Aya, Mariana Benegas, Nuria Viñolas, Roxana Reyes, Ivan Vollmer, Ainara Arcocha, Marcelo Sánchez and Noemi Reguart

Cancers 2021, 13(21), 5566; https://doi.org/10.3390/cancers13215566 - 06 Nov 2021

Cited by 2 | Viewed by 2290

Abstract

Background: The role of perfusion computed tomography (pCT) in detecting changes in tumor vascularization as part of a response to antiangiogenic therapy in non-small cell lung cancer (NSCLC) remains unclear. Methods: In this prospective pilot study (IMPACT trial, NCT02316327), we aimed to determine [...] Read more.

Background: The role of perfusion computed tomography (pCT) in detecting changes in tumor vascularization as part of a response to antiangiogenic therapy in non-small cell lung cancer (NSCLC) remains unclear. Methods: In this prospective pilot study (IMPACT trial, NCT02316327), we aimed to determine the ability of pCT to detect early changes in blood flow (BF), blood volume (BV), and permeability (PMB), and to explore whether these changes could predict the response at day +42 in patients with advanced, treatment-naive, non-squamous NSCLC treated with cisplatin and gemcitabine plus bevacizumab. Results: All of the perfusion parameters showed a consistent decrease during the course of treatment. The BV difference between baseline and early assessment was significant (p = 0.013), whereas all perfusion parameters showed significant differences between baseline and day +42 (p = 0.003, p = 0.049, and p = 0.002, respectively). Among the 16 patients evaluable for efficacy, a significant decline in BV at day +7 from baseline was observed in tumors with no response (p = 0.0418). Conclusions: Our results confirm that pCT can capture early changes in tumor vasculature. A substantial early decline of BV from baseline might identify tumors less likely responsive to antiangiogenic-drugs. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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17 pages, 1792 KiB

Open AccessArticle

Dynamic ¹¹C-Methionine PET-CT: Prognostic Factors for Disease Progression and Survival in Patients with Suspected Glioma Recurrence

by Maria Vittoria Mattoli, Gianluca Trevisi, Valentina Scolozzi, Amedeo Capotosti, Fabrizio Cocciolillo, Irene Marini, Valerio Mare, Luca Indovina, Massimo Caulo, Antonella Saponiero, Mario Balducci, Silvia Taralli and Maria Lucia Calcagni

Cancers 2021, 13(19), 4777; https://doi.org/10.3390/cancers13194777 - 24 Sep 2021

Cited by 5 | Viewed by 1806

Abstract

Purpose: The prognostic evaluation of glioma recurrence patients is important in the therapeutic management. We investigated the prognostic value of ¹¹C-methionine PET-CT (MET-PET) dynamic and semiquantitative parameters in patients with suspected glioma recurrence. Methods: Sixty-seven consecutive patients who underwent MET-PET for suspected [...] Read more.

Purpose: The prognostic evaluation of glioma recurrence patients is important in the therapeutic management. We investigated the prognostic value of ¹¹C-methionine PET-CT (MET-PET) dynamic and semiquantitative parameters in patients with suspected glioma recurrence. Methods: Sixty-seven consecutive patients who underwent MET-PET for suspected glioma recurrence at MR were retrospectively included. Twenty-one patients underwent static MET-PET; 46/67 underwent dynamic MET-PET. In all patients, SUVmax, SUVmean and tumour-to-background ratio (T/B) were calculated. From dynamic acquisition, the shape and slope of time-activity curves, time-to-peak and its SUVmax (SUVmax_TTP) were extrapolated. The prognostic value of PET parameters on progression-free (PFS) and overall survival (OS) was evaluated using Kaplan–Meier survival estimates and Cox regression. Results: The overall median follow-up was 19 months from MET-PET. Recurrence patients (38/67) had higher SUVmax (p = 0.001), SUVmean (p = 0.002) and T/B (p < 0.001); deceased patients (16/67) showed higher SUVmax (p = 0.03), SUVmean (p = 0.03) and T/B (p = 0.006). All static parameters were associated with PFS (all p < 0.001); T/B was associated with OS (p = 0.031). Regarding kinetic analyses, recurrence (27/46) and deceased (14/46) patients had higher SUVmax_TTP (p = 0.02, p = 0.01, respectively). SUVmax_TTP was the only dynamic parameter associated with PFS (p = 0.02) and OS (p = 0.006). At univariate analysis, SUVmax, SUVmean, T/B and SUVmax_TTP were predictive for PFS (all p < 0.05); SUVmax_TTP was predictive for OS (p = 0.02). At multivariate analysis, SUVmax_TTP remained significant for PFS (p = 0.03). Conclusion: Semiquantitative parameters and SUVmax_TTP were associated with clinical outcomes in patients with suspected glioma recurrence. Dynamic PET-CT acquisition, with static and kinetic parameters, can be a valuable non-invasive prognostic marker, identifying patients with worse prognosis who require personalised therapy. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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11 pages, 1970 KiB

Open AccessArticle

18F-FDOPA PET/CT SUV-Derived Indices and Volumetric Parameters Correlation in Patients with Primary Brain Tumors

by Agostino Chiaravalloti, Maria Ricci, Andrea Cimini, Francesca Russo, Francesco Ursini, Luca Filippi and Orazio Schillaci

Cancers 2021, 13(17), 4315; https://doi.org/10.3390/cancers13174315 - 26 Aug 2021

Cited by 1 | Viewed by 2249

Abstract

Novel parameters in PET imaging, such as volumetric parameters, are gaining interest in the scientific literature, but the role of dopaminergic tumor volume (DTV) and total lesion F-DOPA activity (TLDA) and the correlation between volumetric and SUV-derived parameters are not well defined yet. [...] Read more.

Novel parameters in PET imaging, such as volumetric parameters, are gaining interest in the scientific literature, but the role of dopaminergic tumor volume (DTV) and total lesion F-DOPA activity (TLDA) and the correlation between volumetric and SUV-derived parameters are not well defined yet. One hundred and thirty-three patients that underwent 18F-FDOPA imaging for primary brain tumors were included in this retrospective study. SUV-derived indices were calculated (the occipital region was chosen to generate ratios of tumor SUV) and compared with volumetric parameters. Regression models were applied in univariate analysis and lnSUVmax was positively associated with lnDTV (beta 0.42, p = 0.007), the lnSUVmax ratio was positively associated with lnDTV (beta 0.80, p = 0.011), lnSUVmax was positively associated with lnTLDA (beta 1.27, p < 0.0001), and the lnSUVmax ratio was positively associated with lnTLDA (beta 1.87, p < 0.0001). Our study demonstrates that volumetric uptake parameters in 18F-FDOPA PET/CT are easier to assess in primary brain tumors with higher SUV max and SUV max ratios, and supports the emerging role of volumetric parameters in the data interpretation. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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Review

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16 pages, 772 KiB

Open AccessReview

Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients

by Carolina de la Pinta, María E. Castillo, Manuel Collado, Cristina Galindo-Pumariño and Cristina Peña

Cancers 2021, 13(21), 5547; https://doi.org/10.3390/cancers13215547 - 05 Nov 2021

Cited by 17 | Viewed by 3134

Abstract

Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, [...] Read more.

Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination of radiomics and molecular data, called radiogenomics, has clear implications for cancer patients’ management. Though some studies have focused on radiogenomics signatures in hepatocellular carcinoma patients, only a few have examined colorectal cancer metastatic lesions in the liver. Moreover, the need to differentiate between liver lesions is fundamental for accurate diagnosis and treatment. In this review, we summarize the knowledge gained from radiomics and radiogenomics studies in hepatic metastatic colorectal cancer patients and their use in early diagnosis, response assessment and treatment decisions. We also investigate their value as possible prognostic biomarkers. In addition, the great potential of image mining to provide a comprehensive view of liver niche formation is examined thoroughly. Finally, new challenges and current limitations for the early detection of the liver premetastatic niche, based on radiomics and radiogenomics, are also discussed. Full article

(This article belongs to the Special Issue Quantitative Imaging Dynamic Models in Cancer Research)

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