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Kokaine L, Gardovskis A, Gardovskis J. Evaluation and Predictive Factors of Complete Response in Rectal Cancer after Neoadjuvant Chemoradiation Therapy. ACTA ACUST UNITED AC 2021; 57:medicina57101044. [PMID: 34684080 PMCID: PMC8537499 DOI: 10.3390/medicina57101044] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/16/2021] [Accepted: 09/23/2021] [Indexed: 12/18/2022]
Abstract
The response to neoadjuvant chemoradiation therapy is an important prognostic factor for locally advanced rectal cancer. Although the majority of the patients after neoadjuvant therapy are referred to following surgery, the clinical data show that complete clinical or pathological response is found in a significant proportion of the patients. Diagnostic accuracy of confirming the complete response has a crucial role in further management of a rectal cancer patient. As the rate of clinical complete response, unfortunately, is not always consistent with pathological complete response, accurate diagnostic parameters and predictive markers of tumor response may help to guide more personalized treatment strategies and identify potential candidates for nonoperative management more safely. The management of complete response demands interdisciplinary collaboration including oncologists, radiotherapists, radiologists, pathologists, endoscopists and surgeons, because the absence of a multidisciplinary approach may compromise the oncological outcome. Prediction and improvement of rectal cancer response to neoadjuvant therapy is still an active and challenging field of further research. This literature review is summarizing the main, currently known clinical information about the complete response that could be useful in case if encountering such condition in rectal cancer patients after neoadjuvant chemoradiation therapy, using as a source PubMed publications from 2010–2021 matching the search terms “rectal cancer”, “neoadjuvant therapy” and “response”.
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Affiliation(s)
- Linda Kokaine
- Department of Surgery, Riga Stradins University, Dzirciema Street 16, LV-1007 Riga, Latvia; or
- Pauls Stradins Clinical University Hospital, Pilsoņu Street 13, LV-1002 Riga, Latvia
- Correspondence: (L.K.); (J.G.); Tel.: +371-2635-9472 (L.K.)
| | - Andris Gardovskis
- Department of Surgery, Riga Stradins University, Dzirciema Street 16, LV-1007 Riga, Latvia; or
- Pauls Stradins Clinical University Hospital, Pilsoņu Street 13, LV-1002 Riga, Latvia
| | - Jānis Gardovskis
- Department of Surgery, Riga Stradins University, Dzirciema Street 16, LV-1007 Riga, Latvia; or
- Pauls Stradins Clinical University Hospital, Pilsoņu Street 13, LV-1002 Riga, Latvia
- Correspondence: (L.K.); (J.G.); Tel.: +371-2635-9472 (L.K.)
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Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer. Eur Radiol 2021; 31:7031-7038. [PMID: 33569624 DOI: 10.1007/s00330-021-07724-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/24/2020] [Accepted: 01/27/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data. METHODS Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2Wvolume/T2Wentropy/ADCmean/SUVmean/TLG/CTmean-HU) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset. RESULTS When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture). CONCLUSION In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research. KEY POINTS • Quantification of local tumour texture on pre-therapy FDG-PET/CT and MRI has potential added value compared to global tumour features to predict response to chemoradiotherapy in rectal cancer. • However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture over global tumour features is limited. • Predictive performance of our optimal model-combining clinical baseline variables with global quantitative tumour features-was encouraging (AUC 0.83), warranting further research in this direction on a larger scale.
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Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy. Sci Rep 2020; 10:12555. [PMID: 32724164 PMCID: PMC7387337 DOI: 10.1038/s41598-020-69345-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) provides them with the optimal prognosis. However, no reliable prediction model is presently available. We evaluated the performance of an artificial neural network (ANN) model in pCR prediction in patients with LARC. Predictive accuracy was compared between the ANN, k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes classifier (NBC), and multiple logistic regression (MLR) models. Data from two hundred seventy patients with LARC were used to compare the efficacy of the forecasting models. We trained the model with an estimation data set and evaluated model performance with a validation data set. The ANN model significantly outperformed the KNN, SVM, NBC, and MLR models in pCR prediction. Our results revealed that the post-CRT carcinoembryonic antigen is the most influential pCR predictor, followed by intervals between CRT and surgery, chemotherapy regimens, clinical nodal stage, and clinical tumor stage. The ANN model was a more accurate pCR predictor than other conventional prediction models. The predictors of pCR can be used to identify which patients with LARC can benefit from watch-and-wait approaches.
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Kalisz KR, Enzerra MD, Paspulati RM. MRI Evaluation of the Response of Rectal Cancer to Neoadjuvant Chemoradiation Therapy. Radiographics 2020; 39:538-556. [PMID: 30844347 DOI: 10.1148/rg.2019180075] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
MRI plays a critical role in the staging and restaging of rectal cancer. Although newly diagnosed early-stage rectal cancers may immediately be amenable to surgical resection, patients with advanced disease first undergo neoadjuvant therapy that consists of a combination of chemotherapy and radiation therapy. Evaluation of rectal cancer after neoadjuvant therapy is best performed with MRI, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In this setting, MRI allows accurate evaluation of primary tumor staging, which is determined on the basis of the depth of invasion within and through the rectal wall and the involvement of adjacent organs. MRI can also be used to evaluate posttreatment morphologic components within the tumors, including fibrosis and mucinous changes that have been shown to correlate with the response to treatment. Additional features such as the circumferential resection margin and extramural vascular invasion-factors shown to affect prognosis and local recurrence-are also assessed before and after therapy. Functional assessment with diffusion-weighted MRI and perfusion MRI plays a role in predicting tumor aggressiveness and the likelihood of response to treatment, as well as the extent of residual tumor after therapy. Lymph node staging is also performed at MRI, with assessment of not only lymph node size but also the internal architecture and signal intensity characteristics. ©RSNA, 2019 See discussion on this article by Wasnik and Al-Hawary .
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Affiliation(s)
- Kevin R Kalisz
- From the Department of Radiology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH 44106
| | - Michael D Enzerra
- From the Department of Radiology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH 44106
| | - Raj M Paspulati
- From the Department of Radiology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH 44106
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Lv W, Yuan Q, Wang Q, Ma J, Jiang J, Yang W, Feng Q, Chen W, Rahmim A, Lu L. Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT. Eur Radiol 2018. [PMID: 29520429 DOI: 10.1007/s00330-018-5343-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
OBJECTIVES To investigate the impact of parameter settings as used for the generation of radiomics features on their robustness and disease differentiation (nasopharyngeal carcinoma (NPC) versus chronic nasopharyngitis (CN) in FDG PET/CT imaging). METHODS We studied 106 patients (69/37 NPC/CN, pathology confirmed), and extracted 57 radiomics features under different parameter settings. Robustness was assessed by the intra-class correlation coefficient (ICC). Logistic regression with leave-one-out cross validation was used to generate classification probabilities, and diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS Varying averaging strategies and symmetry, 4/26 GLCM features showed poor range of pairwise ICCs of 0.02-0.98, while depicting good AUCs of 0.82-0.91. Varying distances, 5/26 GLCM features showed ICCs of 0.82-0.99 while corresponding AUCs were 0.52-0.91. 6/13 GLRLM features showed both high AUC (0.81-0.89) and high ICC (0.85-0.99) regarding to averaging strategies. 7/13 GLSZM features showed AUCs of 0.81-0.90 while having ICCs of 0.01-0.99 under different neighbourhoods. 2/5 NGTDM features showed AUCs of 0.81-0.85 while having ICCs of 0.19-0.89 for different window sizes. Differentiating a subset of NPC (stages I-II) form CN, both SumEntropy and SZLGE achieved significantly higher AUCs than metabolically active tumour volume (AUC: 0.91 vs. 0.72, p<0.01). CONCLUSIONS Radiomics features depicting poor absolute-scale robustness regarding to parameter settings can still lead to good diagnostic performance. As such, robustness of radiomics features should not be overemphasized for removal of features towards assessment of clinical tasks. For differentiating NPC from CN, some radiomics features (e.g. SumEntropy, SZLGE, LGZE) outperformed conventional metrics. KEY POINTS • Poor robustness did not necessarily translate into poor differentiation performance. • Absolute-scale robustness of radiomics features should not be overemphasized. • Radiomics features SumEntropy, SZLGE and LGZE outperformed conventional metrics.
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Affiliation(s)
- Wenbing Lv
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Jianhua Ma
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China.
| | - Jun Jiang
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Wei Yang
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Qianjin Feng
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Wufan Chen
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Arman Rahmim
- Department of Radiology, Johns Hopkins University, 601 N. Caroline St, Baltimore, MD, 21287, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, 3101 Wyman Park Drive, Baltimore, MD, 21218, USA
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China.
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Goh V, Prezzi D, Mallia A, Bashir U, Stirling JJ, John J, Charles-Edwards G, MacKewn J, Cook G. Positron Emission Tomography/Magnetic Resonance Imaging of Gastrointestinal Cancers. Semin Ultrasound CT MR 2016; 37:352-7. [PMID: 27342899 DOI: 10.1053/j.sult.2016.02.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
As an integrated system, hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) is able to provide simultaneously complementary high-resolution anatomic, molecular, and functional information, allowing comprehensive cancer phenotyping in a single imaging examination. In addition to an improved patient experience by combining 2 separate imaging examinations and streamlining the patient pathway, the superior soft tissue contrast resolution of MRI and the ability to acquire multiparametric MRI data is advantageous over computed tomography. For gastrointestinal cancers, this would improve tumor staging, assessment of neoadjuvant response, and of the likelihood of a complete (R0) resection in comparison with positron emission tomography or computed tomography.
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Affiliation(s)
- Vicky Goh
- Cancer Imaging Department, Division of Imaging Sciences & Biomedical Engineering, King׳s College London, London, UK; Department of Radiology, Guy׳s & St Thomas׳ Hospitals, London, UK.
| | - Davide Prezzi
- Cancer Imaging Department, Division of Imaging Sciences & Biomedical Engineering, King׳s College London, London, UK; Department of Radiology, Guy׳s & St Thomas׳ Hospitals, London, UK
| | - Andrew Mallia
- Cancer Imaging Department, Division of Imaging Sciences & Biomedical Engineering, King׳s College London, London, UK; Guys and St Thomas׳ PET Centre, St Thomas׳ Hospital, King׳s College London, London, UK
| | - Usman Bashir
- Cancer Imaging Department, Division of Imaging Sciences & Biomedical Engineering, King׳s College London, London, UK; Department of Radiology, Guy׳s & St Thomas׳ Hospitals, London, UK
| | - J James Stirling
- Guys and St Thomas׳ PET Centre, St Thomas׳ Hospital, King׳s College London, London, UK
| | - Joemon John
- Guys and St Thomas׳ PET Centre, St Thomas׳ Hospital, King׳s College London, London, UK
| | - Geoff Charles-Edwards
- Cancer Imaging Department, Division of Imaging Sciences & Biomedical Engineering, King׳s College London, London, UK; Medical Physics, Guy׳s and St Thomas׳ Hospitals, London, UK
| | - Jane MacKewn
- Guys and St Thomas׳ PET Centre, St Thomas׳ Hospital, King׳s College London, London, UK
| | - Gary Cook
- Cancer Imaging Department, Division of Imaging Sciences & Biomedical Engineering, King׳s College London, London, UK; Guys and St Thomas׳ PET Centre, St Thomas׳ Hospital, King׳s College London, London, UK
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