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Morelli, I. Neoadjuvant Radiotherapy and Radiomics. Encyclopedia. Available online: (accessed on 18 June 2024).
Morelli I. Neoadjuvant Radiotherapy and Radiomics. Encyclopedia. Available at: Accessed June 18, 2024.
Morelli, Ilaria. "Neoadjuvant Radiotherapy and Radiomics" Encyclopedia, (accessed June 18, 2024).
Morelli, I. (2021, August 04). Neoadjuvant Radiotherapy and Radiomics. In Encyclopedia.
Morelli, Ilaria. "Neoadjuvant Radiotherapy and Radiomics." Encyclopedia. Web. 04 August, 2021.
Neoadjuvant Radiotherapy and Radiomics

Radiotherapy (RT) represents one of the most effective anticancer agents, which can be used either alone or in combination with other strategies (surgery, chemotherapy, and immunotherapy). Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. Neoadjuvant strategy, specifically, represents a type of induction therapy usually given as a first step to shrink a tumor before the main treatment, usually surgery, is given. In this setting, radiotherapy can be used either alone or in combination with chemotherapy. Radiomics in the field of neoadjuvant Radiotherapy can help in the choice of the best subsequent and tailored treatment strategy.

radiomics neoadjuvant radiotherapy texture analysis

1. Texture Analysis in Neoadjuvant Radiotherapy—A Focus on Esophageal Cancer

A trimodal approach with neoadjuvant chemoradiotherapy (nCRT) followed by surgery is the treatment of choice in locally advanced esophageal cancer (EC).

A clinical evaluation of the response is made with different imaging techniques (MRI, CT, and PET) which can classify the response according to the RECIST criteria [1][2][3][4][5][6]. With these premises, radiomic analysis has been used in this setting with the aim to predict the response to neoadjuvant strategy [3].

In patients treated for locally advanced EC, temporal changes in tumor volume (before and after nCRT) were related to pathological response, although the predictive value of this parameter was modest, with no correlation with overall survival [1].

Sun et al. [3] found that several histogram parameters calculated on DCE-MRI can be used in evaluating and predicting the nCRT response.

CT images are mostly used to extract morphologic information of EC, but recent studies suggest that quantitative image features can provide additional information correlated to tumor response and prognosis [7][8][9].

On the other hand, metabolic imaging has been found to be more accurate in evaluating the response to nCRT. Most 18F-FDG PET studies in EC quantify metabolic tumor activity solely by using the maximum standardized uptake value (SUVmax) [10][11]. However, this approach does not characterize the total activity nor heterogeneity of the 18F-FDG uptake for the entire tumor [12][13]. Recent studies suggest that spatial image information provides additional information than SUVmax [14][15][16][17].

It has been hypothesized that tumors could be rendered more homogeneous following treatment due to a reduction in cellular density and interstitial pressure and to normalization of the vasculature with improved intra-tumor perfusion and oxygenation [9]. Studies that performed imaging before and after treatment reported that tumor heterogeneity generally decreased following treatment [9][18]. Simoni et al. found that metabolic TV (MTV) in poor responders was significantly higher than in good responders (38.6 mL vs. 17.7 mL, p = 0.02) [19].

Yip et al. reported that delta-radiomics calculated within MTV60% were the least correlated with pathological response. Instead, temporal changes in tumor 18F-FDG distribution after nCRT assessed by delta-second-order radiomics (ΔRLM textures) were correlated to the OS [20].

In a study of Beukinga et al., the most predictive textural features were Long Run Low Gray-Level (LRLGLe)-PET and Run-Percentage (RP)-CT. LRLGLe-PET depends on long runs (coarse texture) with low gray levels and was higher (i.e., low and homogeneous 18F-FDG uptake) for complete responders and lower (i.e., high and heterogeneous 18F-FDG uptake) for incomplete responders, possibly due to tumor hypoxia and necrosis [21].

In a French study, Hatt et al. assessed the value of MTV, entropy, dissimilarity, high-intensity large-area emphasis, and zone percentage for the prediction of OS of 112 patients with EC who underwent definitive CRT or nCRT followed by surgery [22]. MTV and heterogeneity had a less prognostic value in EC (vs. non-small cell lung cancer), which was attributed to smaller overall volumes. The local dissimilarity parameter appeared most predictive for OS.

Current research is focused on a correlation between the biological tumor markers from pre-treatment tumor biopsies and F-FDG PET-based radiomic features. The prediction model from Beukinga et al. tried to correlate HER2 and the CD44 expression and F-FDG PET-based radiomic features in predicting a complete response (pCR) to nCRT; it showed promising ability to identify pCR [23]. Analysis of texture within tumors on medical imaging, such as CT, MRI, and PET, is emerging as a potential biomarker to predict prognosis and treatment response in patients with EC. These findings could increase our capability to predict and evaluate the nCRT response and optimize treatment planning.

2. Texture Analysis in Neoadjuvant Radiotherapy—A Focus on Lung Cancer

Lung cancer represents the leading cause of cancer death and its treatment rely on different strategies, often used in combination, such as radiotherapy, surgery, chemotherapy, and immunotherapy [24][25][26][27]. Medical imaging is pivotal in all the different phases of lung cancer management, from diagnosis to the evaluation of the efficacy of different approaches, as well as in the subsequent follow-up [28][29][30][31][32][33][34][35].

A significant body of literature is available regarding the role of textural analysis in lung cancer. Radiomic features have been used both for diagnosis (discrimination of malignant nature of lung nodules, histology prediction, and genomic analysis) and for outcome prediction, particularly after definitive chemo-radiotherapy (CT-RT) or stereotactic body radiation therapy (SBRT) [36][37][38][39][40][41][42][43][44][45]. On the other hand, few studies focused on neoadjuvant radiotherapy, possibly because of the controversial benefit of adding surgery after CT-RT [46][47]. The largest available series included 127 stage II–III NSCLC patients treated with neoadjuvant chemoradiotherapy (CT-RT) followed by surgical resection. Texture analysis was conducted on primary tumors with features extracted from planning CT. The endpoint of the analysis was a pathologic response and gross residual disease. Seven features (one shape feature, three second-order features, and three higher-order features) were predictive for pathologic gross residual disease (AUC > 0.6, p-value < 0.05) and one for pathologic complete response (one higher-order feature) (AUC = 0.63, p-value = 0.01). These features suggested that a spherical disproportionality of the primary tumor site (i.e., more complex shape) was a predictor of pathological response, while spherical tumors and tumors with large flat zones were more likely to be linked with residual disease [48]. The same group conducted another analysis on a similar population, analyzing also features from pathologic lymph nodes. Radiomics analysis was performed on 85 primary tumors and 178 lymph nodes with the same endpoints as above (pathologic response and gross residual disease). A mix of clinical variables and conventional and radiomic features were used to create a predictive model. The model built from the radiomic features was the best at predicting pathologic response (AUC 0.68, p < 0.05), while a mixed clinical and radiomic model was better performing for gross residual disease (AUC 0.73, p < 0.05). The authors concluded that the features extracted from the lymph nodes were more informative than those derived by primary tumors [49].

A small study analyzing PET-CT included 13 patients on a prospective clinical trial of trimodal therapy for resectable locally advanced NSCLC. A true texture analysis was not performed; however, pre- and post-chemo-radiotherapy SUV was not correlated with any clinical endpoint (pathologic complete response, PFS, and OS) [50].

Chong et al. performed a texture analysis on a mixed population with NSCLC treated with neoadjuvant chemo-radiotherapy (CT-RT) or neoadjuvant EGFR-inhibitor. Focusing on the CT-RT group, 28 patients were included in the study. A pre-operative CT was used for analysis, and the endpoint again was a pathologic response. In the univariate analysis, tumor volume, mass, kurtosis, and skewness were significant predictors of pathologic response, although only kurtosis maintained its significance in the multivariate analysis (OR 1.107, p = 0.009). ROC analysis showed that the AUC for kurtosis was 0.943 and that the optimal cut-off value of percent change of kurtosis for predicting pathologic response was less than −23 (sensitivity, 87.5%; specificity, 84.3%) [51].

More recently, Khorrami et al. conducted a textural analysis on 90 stage IIIA NSCLC patients treated with neoadjuvant CT-RT, followed by surgical resection. Pretreatment CT scans were used for features extraction. Patients were randomly split into two sets, one for training and one for testing.

Interestingly, the authors analyzed the features not only from primary tumors, but also from the peritumoral region (defined as a 15 mm dilation from primary nodule) to identify a possible predicting role of the tumor microenvironment.

Thirteen intratumoral and peritumoral radiomic texture features were found to be predictive of major pathologic response and were used to define a classifier with an AUC of 0.90 ± 0.025 within the training set, and an AUC = 0.86 in the test set. This signature was also predictive for OS (HR = 11.18, 95% CI = 3.17, 44.1; p-value = 0.008) and DFS (HR = 2.78, 95% CI = 1.11, 4.12; p-value = 0.0042) in the testing set. The combination of peritumoral and intratumoral features performed better than a clustering built only on intratumoral features.

The results of this study highlight that heterogeneous enhancement and disruption of textural patterns within and outside the nodules (better identified by Law Laplacian and Laws features) can predict not only response, but also patients’ prognosis [52].

Identifying patients who respond completely to chemoradiation and who do not require additional invasive local therapy, also considering the controversial role of surgery in this scenario, is an unmet clinical need.

The intrinsic potential of texture analysis in predicting a response after neoadjuvant CT-RT is plain. However, available studies are not enough to consider radiomics as a ready and easily accessible clinical biomarker. Larger studies, with a higher number of patients and with external validation, are needed before radiomic analysis could be clinically implemented. Analyzing available data, a special attention in future research should be paid to nodal and peritumoral features, which are potentially even more predictive than primary tumor analysis.

3. Texture Analysis in Neoadjuvant Radiotherapy—A Focus on Sarcoma

Soft tissue sarcomas (STS) are a group of mesenchymal malignancies encompassing a wide array of distinct clinical entities, including over 50 different histological subtypes [53]. Due to their rarity and non-specific clinical presentation (mostly consisting of slowly-growing indolent swellings in limbs or trunk), a differential diagnosis with benign tumors is challenging and may result in a significant delay to curative treatment, consisting of wide surgical excision in absence of metastatic dissemination [53][54]. Furthermore, their intrinsic heterogeneity is also displayed by variable levels of genomic profile complexity and structural architecture, resulting in different clinical presentations and inconsistent responses to standard treatments [54][55][56]. Unfortunately, one among the major predictors of outcome in operable patients—tumor histological grading—may be underestimated by tumor biopsy [57]. This is a relevant issue, since tumor grade determination may guide treatment management: low-grade STSs may be treated with planned marginal excision, sparing patients from more extensive surgery [58], while detection of high-grade disease may serve as a decision criteria for pre-operative treatment intensification [59][60]. Moreover, while the additional benefit of adjuvant chemotherapy is questioned and limited to a subset of high-risk patients, no consensual criteria have been provided to define this population; clinical [61] and genomic [62] classifiers have been proposed, although their use have not been implemented in clinical practice yet.

Hence, there is an urgent need to identify novel and readily available biomarkers to improve diagnosis, predict the disease course, and possibly define a tailored pattern of care in patients at high risk of disease relapse [54]. Radiomics may provide a valuable source of information in STS patients. Due to its non-invasive nature, radiomics may quickly discern benign from malignant lesions, timely addressing the need for further investigation; secondarily, it may provide an appropriate histopathologic grade determination on the whole tumor bulk, thus overcoming the limitations of tumor biopsy in accounting for intratumor heterogeneity; finally, identification of specific signatures correlated with outcome may allow prediction of relapse risk and eligibility for further interventions.

Improvement in differential diagnosis of malignancy have been reported by several authors [63][64]. In particular radiomics-based differentiation between soft-tissue lipoma and well-differentiated liposarcoma [65][66] was demonstrated despite similar radiologic and pathologic presentation, often requiring molecular analysis of MDM2 amplification status; interestingly, superior performance of a machine-learning classifier as compared to trained radiologists has been shown [67]. Similarly, radiomic features allowed to distinguish myxoma from myxofibrosarcoma [68] and atypical leiomyoma from uterine sarcoma [69][70][71].

Machine-learning algorithms proved also useful in the prediction of histopathologic grade. Using CT- or MRI-based radiomics, different signatures associated with high-grade disease were identified [72][73][74][75][76][77][78]; in some cases, integration with clinical features resulted in the establishment of a prognostic nomogram for risk stratification [74].

A radiomic approach may be used to improve prediction of patients’ outcome. RM texture analysis alone [79][80] or combined with PET/CT metabolic data [81][82] was associated with metastatic relapse and specific signatures were identified for prediction of survival [83][84]. Radiomic analysis was also applied on surveillance MRI in patients undergoing follow-up after surgical resection [85], resulting in improved detection and characterization of local recurrence [86].

Concerning prediction of sensitivity to treatment, preliminary results reported adequate concordance between radiomic features and response to neoadjuvant chemotherapy [87] or chemoradiation [88], as well as exclusive chemotherapy in unresectable patients [85][89].

On the other hand, while radiomics analysis correlated with adipocytic maturation following neoadjuvant chemotherapy in myxoid/round cells liposarcomas, it occurred independently from the chemotherapy regimen and was not correlated with metastasis-free survival [90]; similarly, radiomics was insufficient to predict the response from hypofractionated pre-operative RT [91].

Despite promising preliminary results, no radiomic signature for sarcoma has been implemented to date in clinical practice. Multiple issues have been detected in the pathway to translation from exploratory analysis into standard of care, ranging from lack of external validation and replicability, consistency, and cost-effectiveness of the imaging biomarkers proposed at present [92]. While these shortcomings are partially motivated by the complex workflow and relevant technical requirements needed for the development and elaboration of radiomic signatures, further effort is required for successful translation into clinical use.


  1. Alfieri, R.; Pintacuda, G.; Cagol, M.; Occhipinti, T.; Capraro, I.; Scarpa, M.; Zanchettin, G.; Cavallin, F.; Michelotto, M.; Giacomelli, L.; et al. Oesophageal cancer: Assessment of tumour response to chemoradiotherapy with tridimensional CT. Radiol. Med. 2015, 120, 430–439.
  2. Giganti, F.; Salerno, A.; Ambrosi, A.; Chiari, D.; Orsenigo, E.; Esposito, A.; Albarello, L.; Mazza, E.; Staudacher, C.; Del Maschio, A.; et al. Prognostic utility of diffusion-weighted MRI in oesophageal cancer: Is apparent diffusion coefficient a potential marker of tumour aggressiveness? Radiol. Med. 2016, 121, 173–180.
  3. Sun, N.N.; Ge, X.L.; Liu, X.S.; Xu, L.L. Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma. Radiol. Med. 2020, 125, 165–176.
  4. Bi, Y.; Zhu, X.; Yu, Z.; Wu, G.; Han, X. Interventional radiology protocol for treatment of esophagogastric anastomotic leakage. Radiol. Med. 2019, 124, 1253–1261.
  5. Bi, Y.; Zhu, X.; Yu, Z.; Jiao, D.; Yi, M.; Han, X. Radioactive feeding tube in the palliation of esophageal malignant obstruction. Radiol. Med. 2020, 125, 544–550.
  6. Borghetti, P.; Bonu, M.L.; Giubbolini, R.; Levra, N.G.; Mazzola, R.; Perna, M.; Visani, L.; Meacci, F.; Taraborrelli, M.; Triggiani, L.; et al. Concomitant radiotherapy and TKI in metastatic EGFR- or ALK-mutated non-small cell lung cancer: A multicentric analysis on behalf of AIRO lung cancer study group. Radiol. Med. 2019, 124, 662–670.
  7. Ganeshan, B.; Skogen, K.; Pressney, I.; Coutroubis, D.; Miles, K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: Preliminary evidence of an association with tumour metabolism, stage, and survival. Clin. Radiol. 2012, 67, 157–164.
  8. Yip, C.; Landau, D.; Kozarski, R.; Ganeshan, B.; Thomas, R.; Michaelidou, A.; Goh, V. Primary esophageal cancer: Heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy. Radiology 2014, 270, 141–148.
  9. Yip, C.; Davnall, F.; Kozarski, R.; Landau, D.B.; Cook, G.J.; Ross, P.; Mason, R.; Goh, V. Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer. Dis. Esophagus 2015, 28, 172–179.
  10. Kwee, R.M. Prediction of tumor response to neoadjuvant therapy in patients with esophageal cancer with use of 18F FDG PET: A systematic review. Radiology 2010, 254, 707–717.
  11. Zhu, W.; Xing, L.; Yue, J.; Sun, X.; Sun, X.; Zhao, H.; Yu, J. Prognostic significance of SUV on PET/CT in patients with localised oesophagogastric junction cancer receiving neoadjuvant chemotherapy/chemoradiation:a systematic review and meta-analysis. Br. J. Radiol. 2012, 85, e694–e701.
  12. Zhang, H.; Tan, S.; Chen, W.; Kligerman, S.; Kim, G.; D−Souza, W.D.; Suntharalingam, M.; Lu, W. Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics. Int. J. Radiat. Oncol. Biol. Phys. 2014, 88, 195–203.
  13. Dong, X.; Wu, P.; Sun, X.; Li, W.; Wan, H.; Yu, J.; Xing, L. Intra-tumour 18F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. J. Med. Imaging Radiat. Oncol. 2015, 59, 338–345.
  14. Hatt, M.; Visvikis, D.; Pradier, O.; Cheze-le Rest, C. Baseline (1)(8)F-FDG PET image-derived parameters for therapy response prediction in oesophageal cancer. Eur. J. Nucl. Med. Mol. Imaging 2011, 38, 1595–1606.
  15. El Naqa, I.; Grigsby, P.; Apte, A.; Kidd, E.; Donnelly, E.; Khullar, D.; Chaudhari, S.; Yang, D.; Schmitt, M.; Laforest, R.; et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009, 42, 1162–1171.
  16. Roedl, J.B.; Colen, R.R.; Holalkere, N.S.; Fischman, A.J.; Choi, N.C.; Blake, M.A. Adenocarcinomas of the esophagus: Response to chemoradiotherapy is associated with decrease of metabolic tumor volume as measured on PET-CT. Comparison to histopathologic and clinical response evaluation. Radiother. Oncol. 2008, 89, 278–286.
  17. Blom, R.L.; Steenbakkers, I.R.; Lammering, G.; Vliegen, R.F.; Belgers, E.J.; de Jonge, C.; Schreurs, W.M.; Nap, M.; Sosef, M.N. PET/CT-based metabolic tumour volume for response prediction of neoadjuvant chemoradiotherapy in oesophageal carcinoma. Eur. J. Nucl. Med. Mol. Imaging 2013, 40, 1500–1506.
  18. van Rossum, P.S.; Fried, D.V.; Zhang, L.; Hofstetter, W.L.; van Vulpen, M.; Meijer, G.J.; Court, L.E.; Lin, S.H. The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer. J. Nucl. Med. 2016, 57, 691–700.
  19. Simoni, N.; Rossi, G.; Benetti, G.; Zuffante, M.; Micera, R.; Pavarana, M.; Guariglia, S.; Zivelonghi, E.; Mengardo, V.; Weindelmayer, J.; et al. (18)F-FDG PET/CT Metrics Are Correlated to the Pathological Response in Esophageal Cancer Patients Treated with Induction Chemotherapy Followed by Neoadjuvant Chemo-Radiotherapy. Front. Oncol. 2020, 10, 599907.
  20. Yip, S.S.; Coroller, T.P.; Sanford, N.N.; Mamon, H.; Aerts, H.J.; Berbeco, R.I. Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients. Front. Oncol. 2016, 6, 72.
  21. Beukinga, R.J.; Hulshoff, J.B.; van Dijk, L.V.; Muijs, C.T.; Burgerhof, J.G.M.; Kats-Ugurlu, G.; Slart, R.; Slump, C.H.; Mul, V.E.M.; Plukker, J.T.M. Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment (18)F-FDG PET/CT Imaging. J. Nucl. Med. 2017, 58, 723–729.
  22. Hatt, M.; Majdoub, M.; Vallieres, M.; Tixier, F.; Le Rest, C.C.; Groheux, D.; Hindie, E.; Martineau, A.; Pradier, O.; Hustinx, R.; et al. 18F-FDG PET uptake characterization through texture analysis: Investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J. Nucl. Med. 2015, 56, 38–44.
  23. Beukinga, R.J.; Wang, D.; Karrenbeld, A.; Dijksterhuis, W.P.M.; Faber, H.; Burgerhof, J.G.M.; Mul, V.E.M.; Slart, R.; Coppes, R.P.; Plukker, J.T.M. Addition of HER2 and CD44 to (18)F-FDG PET-based clinico-radiomic models enhances prediction of neoadjuvant chemoradiotherapy response in esophageal cancer. Eur. Radiol. 2021, 31, 3306–3314.
  24. Nardone, V.; Pastina, P.; Giannicola, R.; Agostino, R.; Croci, S.; Tini, P.; Pirtoli, L.; Giordano, A.; Tagliaferri, P.; Correale, P. How to Increase the Efficacy of Immunotherapy in NSCLC and HNSCC: Role of Radiation Therapy, Chemotherapy, and Other Strategies. Front. Immunol. 2018, 9, 2941.
  25. Tini, P.; Nardone, V.; Pastina, P.; Pirtoli, L.; Correale, P.; Giordano, A. The effects of radiotherapy on the survival of patients with unresectable non-small cell lung cancer. Expert Rev. Anticancer Ther. 2018, 18, 593–602.
  26. Parisi, G.; Mazzola, R.; Ciammella, P.; Timon, G.; Fozza, A.; Franceschini, D.; Navarria, F.; Bruni, A.; Perna, M.; Giaj-Levra, N.; et al. Hypofractionated radiation therapy in the management of locally advanced NSCLC: A narrative review of the literature on behalf of the Italian Association of Radiation Oncology (AIRO)-Lung Working Group. Radiol. Med. 2019, 124, 136–144.
  27. Valeriani, M.; Marinelli, L.; Nicosia, L.; Reverberi, C.; De Sanctis, V.; Mollo, D.; Osti, M.F. Locally advanced inoperable primary or recurrent non-small cell lung cancer treated with 4-week hypofractionated radiation therapy (3 Gy/fraction). Radiol. Med. 2019, 124, 1324–1332.
  28. Machado Medeiros, T.; Altmayer, S.; Watte, G.; Zanon, M.; Basso Dias, A.; Henz Concatto, N.; Hoefel Paes, J.; Mattiello, R.; de Souza Santos, F.; Mohammed, T.L.; et al. 18F-FDG PET/CT and whole-body MRI diagnostic performance in M staging for non-small cell lung cancer: A systematic review and meta-analysis. Eur. Radiol. 2020, 30, 3641–3649.
  29. Pak, K.; Park, S.; Cheon, G.J.; Kang, K.W.; Kim, I.J.; Lee, D.S.; Kim, E.E.; Chung, J.K. Update on nodal staging in non-small cell lung cancer with integrated positron emission tomography/computed tomography: A meta-analysis. Ann. Nucl. Med. 2015, 29, 409–419.
  30. Zhang, Y.; Ni, J.; Wei, K.; Tian, J.; Sun, S. CT, MRI, and F-18 FDG PET for the detection of non-small-cell lung cancer (NSCLC): A protocol for a network meta-analysis of diagnostic test accuracy. Medicine 2018, 97, e12387.
  31. Arrigoni, F.; Bruno, F.; Zugaro, L.; Natella, R.; Cappabianca, S.; Russo, U.; Papapietro, V.R.; Splendiani, A.; Di Cesare, E.; Masciocchi, C.; et al. Developments in the management of bone metastases with interventional radiology. Acta Biomed. 2018, 89, 166–174.
  32. Reginelli, A.; Silvestro, G.; Fontanella, G.; Sangiovanni, A.; Conte, M.; Nuzzo, I.; Calvanese, M.; Traettino, M.; Ferraioli, P.; Grassi, R.; et al. Validation of DWI in assessment of radiotreated bone metastases in elderly patients. Int. J. Surg. 2016, 33, S148–S153.
  33. Alessio, N.; Capasso, S.; Di Bernardo, G.; Cappabianca, S.; Casale, F.; Calarco, A.; Cipollaro, M.; Peluso, G.; Galderisi, U. Mesenchymal stromal cells having inactivated RB1 survive following low irradiation and accumulate damaged DNA: Hints for side effects following radiotherapy. Cell Cycle 2017, 16, 251–258.
  34. Nardone, V.; Tini, P.; Pastina, P.; Botta, C.; Reginelli, A.; Carbone, S.F.; Giannicola, R.; Calabrese, G.; Tebala, C.; Guida, C.; et al. Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab. Oncol. Lett. 2020, 19, 1559–1566.
  35. Franceschini, D.; Bruni, A.; Borghetti, P.; Giaj-Levra, N.; Ramella, S.; Buffoni, L.; Badellino, S.; Andolina, M.; Comin, C.; Vattemi, E.; et al. Is multidisciplinary management possible in the treatment of lung cancer? A report from three Italian meetings. Radiol. Med. 2020, 125, 214–219.
  36. Phillips, I.; Ajaz, M.; Ezhil, V.; Prakash, V.; Alobaidli, S.; McQuaid, S.J.; South, C.; Scuffham, J.; Nisbet, A.; Evans, P. Clinical applications of textural analysis in non-small cell lung cancer. Br. J. Radiol. 2018, 91, 20170267.
  37. Sollini, M.; Cozzi, L.; Antunovic, L.; Chiti, A. PET Radiomics in NSCLC: State of the art and a proposal for harmonization of methodology. Sci. Rep. 2017, 7, 358.
  38. Rabbani, M.; Kanevsky, J.; Kafi, K.; Chandelier, F.; Giles, F.J. Role of artificial intelligence in the care of patients with nonsmall cell lung cancer. Eur. J. Clin. Investig. 2018, 48.
  39. Wong, C.W.; Chaudhry, A. Radiogenomics of lung cancer. J. Thorac Dis 2020, 12, 5104–5109.
  40. Shi, L.; He, Y.; Yuan, Z.; Benedict, S.; Valicenti, R.; Qiu, J.; Rong, Y. Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer. Technol. Cancer Res. Treat. 2018, 17, 1533033818782788.
  41. Ninatti, G.; Kirienko, M.; Neri, E.; Sollini, M.; Chiti, A. Imaging-Based Prediction of Molecular Therapy Targets in NSCLC by Radiogenomics and AI Approaches: A Systematic Review. Diagnostics 2020, 10, 359.
  42. Reginelli, A.; Di Grezia, G.; Izzo, A.; D’Andrea, A.; Gatta, G.; Cappabianca, S.; Squillaci, E.; Grassi, R. Imaging of adrenal incidentaloma: Our experience. Int. J. Surg. 2014, 12, S126–S131.
  43. Sun, J.; Hu, D.; Shen, Y.; Yang, H.; Chen, C.; Yin, J.; Peng, Y. Improving image quality with model-based iterative reconstruction algorithm for chest CT in children with reduced contrast concentration. Radiol. Med. 2019, 124, 595–601.
  44. Chang, C.; Sun, X.; Zhao, W.; Wang, R.; Qian, X.; Lei, B.; Wang, L.; Liu, L.; Ruan, M.; Xie, W.; et al. Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (≤3 cm): PET/CT findings and correlations with lymph node metastasis. Radiol. Med. 2020, 125, 257–264.
  45. Zhang, G.; Yang, Z.; Gong, L.; Jiang, S. Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations. Radiol. Med. 2020, 125, 374–383.
  46. Albain, K.S.; Swann, R.S.; Rusch, V.W.; Turrisi, A.T., III; Shepherd, F.A.; Smith, C.; Chen, Y.; Livingston, R.B.; Feins, R.H.; Gandara, D.R.; et al. Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: A phase III randomised controlled trial. Lancet 2009, 374, 379–386.
  47. van Meerbeeck, J.P.; Kramer, G.W.; Van Schil, P.E.; Legrand, C.; Smit, E.F.; Schramel, F.; Tjan-Heijnen, V.C.; Biesma, B.; Debruyne, C.; van Zandwijk, N.; et al. Randomized controlled trial of resection versus radiotherapy after induction chemotherapy in stage IIIA-N2 non-small-cell lung cancer. J. Natl. Cancer Inst. 2007, 99, 442–450.
  48. Coroller, T.P.; Agrawal, V.; Narayan, V.; Hou, Y.; Grossmann, P.; Lee, S.W.; Mak, R.H.; Aerts, H.J. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother. Oncol. 2016, 119, 480–486.
  49. Coroller, T.P.; Agrawal, V.; Huynh, E.; Narayan, V.; Lee, S.W.; Mak, R.H.; Aerts, H. Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC. J. Thorac. Oncol. 2017, 12, 467–476.
  50. Kozak, M.M.; Murphy, J.D.; Schipper, M.L.; Donington, J.S.; Zhou, L.; Whyte, R.I.; Shrager, J.B.; Hoang, C.D.; Bazan, J.; Maxim, P.G.; et al. Tumor volume as a potential imaging-based risk-stratification factor in trimodality therapy for locally advanced non-small cell lung cancer. J. Thorac. Oncol. 2011, 6, 920–926.
  51. Chong, Y.; Kim, J.H.; Lee, H.Y.; Ahn, Y.C.; Lee, K.S.; Ahn, M.J.; Kim, J.; Shim, Y.M.; Han, J.; Choi, Y.L. Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR-TKIs: Are they different from those in neoadjuvant concurrent chemoradiotherapy? PLoS ONE 2014, 9, e88598.
  52. Khorrami, M.; Jain, P.; Bera, K.; Alilou, M.; Thawani, R.; Patil, P.; Ahmad, U.; Murthy, S.; Stephans, K.; Fu, P.; et al. Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer 2019, 135, 1–9.
  53. Sbaraglia, M.; Dei Tos, A.P. The pathology of soft tissue sarcomas. Radiol. Med. 2019, 124, 266–281.
  54. Badalamenti, G.; Messina, C.; De Luca, I.; Musso, E.; Casarin, A.; Incorvaia, L. Soft tissue sarcomas in the precision medicine era: New advances in clinical practice and future perspectives. Radiol. Med. 2019, 124, 259–265.
  55. Greto, D.; Loi, M.; Terziani, F.; Visani, L.; Garlatti, P.; Lo Russo, M.; Teriaca, A.; Muntoni, C.; Delli Paoli, C.; Topulli, J.; et al. A matched cohort study of radio-chemotherapy versus radiotherapy alone in soft tissue sarcoma patients. Radiol. Med. 2019, 124, 301–308.
  56. Greto, D.; Saieva, C.; Loi, M.; Terziani, F.; Visani, L.; Garlatti, P.; Lo Russo, M.; Muntoni, C.; Becherini, C.; Topulli, J.; et al. Influence of age and subtype in outcome of operable liposarcoma. Radiol. Med. 2019, 124, 290–300.
  57. Strauss, D.C.; Qureshi, Y.A.; Hayes, A.J.; Thway, K.; Fisher, C.; Thomas, J.M. The role of core needle biopsy in the diagnosis of suspected soft tissue tumours. J. Surg. Oncol. 2010, 102, 523–529.
  58. Dangoor, A.; Seddon, B.; Gerrand, C.; Grimer, R.; Whelan, J.; Judson, I. UK guidelines for the management of soft tissue sarcomas. Clin. Sarcoma Res. 2016, 6, 20.
  59. Greto, D.; Livi, L.; Saieva, C.; Bonomo, P.; Meattini, I.; Loi, M.; Di Brina, L.; Beltrami, G.; Campanacci, D.; Scoccianti, G.; et al. Neoadjuvant treatment of soft tissue sarcoma. Radiol. Med. 2014, 119, 195–200.
  60. Mangoni, M.; Sottili, M.; Salvatore, G.; Campanacci, D.; Scoccianti, G.; Beltrami, G.; Paoli, C.D.; Dominici, L.; Maragna, V.; Olmetto, E.; et al. Soft tissue sarcomas: New opportunity of treatment with PARP inhibitors? Radiol. Med. 2019, 124, 282–289.
  61. Pasquali, S.; Colombo, C.; Pizzamiglio, S.; Verderio, P.; Callegaro, D.; Stacchiotti, S.; Martin Broto, J.; Lopez-Pousa, A.; Ferrari, S.; Poveda, A.; et al. High-risk soft tissue sarcomas treated with perioperative chemotherapy: Improving prognostic classification in a randomised clinical trial. Eur. J. Cancer 2018, 93, 28–36.
  62. Chibon, F.; Lagarde, P.; Salas, S.; Perot, G.; Brouste, V.; Tirode, F.; Lucchesi, C.; de Reynies, A.; Kauffmann, A.; Bui, B.; et al. Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nat. Med. 2010, 16, 781–787.
  63. Robba, T.; Chianca, V.; Albano, D.; Clementi, V.; Piana, R.; Linari, A.; Comandone, A.; Regis, G.; Stratta, M.; Faletti, C.; et al. Diffusion-weighted imaging for the cellularity assessment and matrix characterization of soft tissue tumour. Radiol. Med. 2017, 122, 871–879.
  64. Fields, B.K.K.; Demirjian, N.L.; Hwang, D.H.; Varghese, B.A.; Cen, S.Y.; Lei, X.; Desai, B.; Duddalwar, V.; Matcuk, G.R., Jr. Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors. Eur. Radiol. 2021.
  65. Leporq, B.; Bouhamama, A.; Pilleul, F.; Lame, F.; Bihane, C.; Sdika, M.; Blay, J.Y.; Beuf, O. MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: A pilot study. Cancer Imaging: Off. Publ. Int. Cancer Imaging Soc. 2020, 20, 78.
  66. Vos, M.; Starmans, M.P.A.; Timbergen, M.J.M.; van der Voort, S.R.; Padmos, G.A.; Kessels, W.; Niessen, W.J.; van Leenders, G.; Grunhagen, D.J.; Sleijfer, S.; et al. Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. Br. J. Surg. 2019, 106, 1800–1809.
  67. Malinauskaite, I.; Hofmeister, J.; Burgermeister, S.; Neroladaki, A.; Hamard, M.; Montet, X.; Boudabbous, S. Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists. Sarcoma 2020, 2020, 7163453.
  68. Martin-Carreras, T.; Li, H.; Cooper, K.; Fan, Y.; Sebro, R. Radiomic features from MRI distinguish myxomas from myxofibrosarcomas. BMC Med. Imaging 2019, 19, 67.
  69. Xie, H.; Hu, J.; Zhang, X.; Ma, S.; Liu, Y.; Wang, X. Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: Comparison on diagnostic efficacy of MRI features and radiomic features. Eur. J. Radiol. 2019, 115, 39–45.
  70. Xie, H.; Zhang, X.; Ma, S.; Liu, Y.; Wang, X. Preoperative Differentiation of Uterine Sarcoma from Leiomyoma: Comparison of Three Models Based on Different Segmentation Volumes Using Radiomics. Mol. Imaging Biol. 2019, 21, 1157–1164.
  71. Wang, T.; Gong, J.; Li, Q.; Chu, C.; Shen, W.; Peng, W.; Gu, Y.; Li, W. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI. Eur. Radiol. 2021.
  72. Yan, R.; Hao, D.; Li, J.; Liu, J.; Hou, F.; Chen, H.; Duan, L.; Huang, C.; Wang, H.; Yu, T. Magnetic Resonance Imaging-Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two-Center Study. J. Magn. Reason. Imaging 2021, 53, 1683–1696.
  73. Xu, W.; Hao, D.; Hou, F.; Zhang, D.; Wang, H. Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade. AJR Am. J. Roentgenol 2020, 215, 963–969.
  74. Peeken, J.C.; Spraker, M.B.; Knebel, C.; Dapper, H.; Pfeiffer, D.; Devecka, M.; Thamer, A.; Shouman, M.A.; Ott, A.; von Eisenhart-Rothe, R.; et al. Tumor grading of soft tissue sarcomas using MRI-based radiomics. EBioMedicine 2019, 48, 332–340.
  75. Wang, H.; Chen, H.; Duan, S.; Hao, D.; Liu, J. Radiomics and Machine Learning with Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas. J. Magn. Reason. Imaging 2020, 51, 791–797.
  76. Peeken, J.C.; Bernhofer, M.; Spraker, M.B.; Pfeiffer, D.; Devecka, M.; Thamer, A.; Shouman, M.A.; Ott, A.; Nusslin, F.; Mayr, N.A.; et al. CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy. Radiother. Oncol. 2019, 135, 187–196.
  77. Zhang, Y.; Zhu, Y.; Shi, X.; Tao, J.; Cui, J.; Dai, Y.; Zheng, M.; Wang, S. Soft Tissue Sarcomas: Preoperative Predictive Histopathological Grading Based on Radiomics of MRI. Acad. Radiol. 2019, 26, 1262–1268.
  78. Corino, V.D.A.; Montin, E.; Messina, A.; Casali, P.G.; Gronchi, A.; Marchiano, A.; Mainardi, L.T. Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J. Magn. Reason. Imaging 2018, 47, 829–840.
  79. Tian, L.; Zhang, D.; Bao, S.; Nie, P.; Hao, D.; Liu, Y.; Zhang, J.; Wang, H. Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas. Clin. Radiol. 2021, 76, 158.e119–158.e125.
  80. Crombe, A.; Le Loarer, F.; Sitbon, M.; Italiano, A.; Stoeckle, E.; Buy, X.; Kind, M. Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas? Eur. Radiol. 2020, 30, 2413–2424.
  81. Peng, Y.; Bi, L.; Guo, Y.; Feng, D.; Fulham, M.; Kim, J. Deep multi-modality collaborative learning for distant metastases predication in PET-CT soft-tissue sarcoma studies. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany, 23–27 July 2019; pp. 3658–3688.
  82. Vallieres, M.; Freeman, C.R.; Skamene, S.R.; El Naqa, I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys. Med. Biol. 2015, 60, 5471–5496.
  83. Peeken, J.C.; Neumann, J.; Asadpour, R.; Leonhardt, Y.; Moreira, J.R.; Hippe, D.S.; Klymenko, O.; Foreman, S.C.; von Schacky, C.E.; Spraker, M.B.; et al. Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics. Cancers 2021, 13, 1929.
  84. Spraker, M.B.; Wootton, L.S.; Hippe, D.S.; Ball, K.C.; Peeken, J.C.; Macomber, M.W.; Chapman, T.R.; Hoff, M.N.; Kim, E.Y.; Pollack, S.M.; et al. MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma. Adv. Radiat. Oncol. 2019, 4, 413–421.
  85. Esser, M.; Kloth, C.; Thaiss, W.M.; Reinert, C.P.; Fritz, J.; Kopp, H.G.; Horger, M. CT-response patterns and the role of CT-textural features in inoperable abdominal/retroperitoneal soft tissue sarcomas treated with trabectedin. Eur. J. Radiol. 2018, 107, 175–182.
  86. Tagliafico, A.S.; Bignotti, B.; Rossi, F.; Valdora, F.; Martinoli, C. Local recurrence of soft tissue sarcoma: A radiomic analysis. Radiol. Oncol. 2019, 53, 300–306.
  87. Crombe, A.; Perier, C.; Kind, M.; De Senneville, B.D.; Le Loarer, F.; Italiano, A.; Buy, X.; Saut, O. T2 -based MRI Delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy. J. Magn. Reason. Imaging 2019, 50, 497–510.
  88. Tian, F.; Hayano, K.; Kambadakone, A.R.; Sahani, D.V. Response assessment to neoadjuvant therapy in soft tissue sarcomas: Using CT texture analysis in comparison to tumor size, density, and perfusion. Abdom. Imaging 2015, 40, 1705–1712.
  89. Esser, M.; Kloth, C.; Thaiss, W.M.; Reinert, C.P.; Kraus, M.S.; Gast, G.C.; Horger, M. CT-morphologic and CT-textural patterns of response in inoperable soft tissue sarcomas treated with pazopanib-a preliminary retrospective cohort study. Br. J. Radiol. 2019, 92, 20190158.
  90. Crombe, A.; Sitbon, M.; Stoeckle, E.; Italiano, A.; Buy, X.; Le Loarer, F.; Kind, M. Magnetic resonance imaging assessment of chemotherapy-related adipocytic maturation in myxoid/round cell liposarcomas: Specificity and prognostic value. Br. J. Radiol. 2020, 93, 20190794.
  91. Gao, Y.; Kalbasi, A.; Hsu, W.; Ruan, D.; Fu, J.; Shao, J.; Cao, M.; Wang, C.; Eilber, F.C.; Bernthal, N.; et al. Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Phys. Med. Biol. 2020, 65, 175006.
  92. Crombe, A.; Fadli, D.; Italiano, A.; Saut, O.; Buy, X.; Kind, M. Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications? Eur J. Radiol. 2020, 132, 109283.
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