Differences in tumor-to-normal organ SUV ratios measured with 68Ga-DOTATATE PET compared with 177Lu-DOTATATE SPECT in patients with neuroendocrine tumors

Wong, Ka Kit; Frey, Kirk A.; Niedbala, Jeremy; Kaza, Ravi K.; Worden, Francis P.; Fitzpatrick, Kellen J.; Dewaraja, Yuni K.

doi:10.1097/mnm.0000000000001592

Cited by 5 publications

(14 citation statements)

References 23 publications

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“…The correlation pertained to SUV ratios among the tumor, liver, and spleen. Similarly, Wong et al 45 . observed a correlation between pretherapy 68 Ga‐DOTATATE PET and posttherapy 177 Lu‐DOTATATE SPECT SUV tumor‐to‐normal organ ratios (liver, spleen, and kidneys) in 24 patients at 4 and 24 h after cycle 1.…”

Section: Introductionmentioning

confidence: 71%

“…The correlation pertained to SUV ratios among the tumor, liver, and spleen. Similarly, Wong et al 45 observed a correlation between pretherapy 68 Ga-DOTATATE PET and posttherapy 177 Lu-DOTATATE SPECT SUV tumor-to-normal organ ratios (liver, spleen, and kidneys) in 24 patients at 4 and 24 h after cycle 1. These studies provide evidence of the potential usefulness of pretherapeutic scans for predicting posttreatment doses or for establishing correlations between post-and pretreatment scans in patients undergoing treatment for NETs.…”

mentioning

confidence: 74%

“…Training our model without considering features extracted from the pretherapy 68 Ga‐DOTATOC PET/CT scans was not investigated. Our focus was on developing a comprehensive pretreatment planning dose predictive model that would incorporate the highly promising predictive ability of the pretherapy scans, as demonstrated in previous studies 42–45 …”

Section: Discussionmentioning

confidence: 99%

“…Also, they did not assess the ability of the pretreatment baseline PET/CT scans to predict the posttreatment absorbed dose, even though it has been shown to be useful for more than only selection purposes. Several studies suggest that the quantitative measurements obtained from pretreatment PET/CT scans using 68 Ga‐DOTA peptides and standard uptake value (SUV) could be indicative of the absorbed dose to the tumor 38,39 or the OARs during PRRT 40–45 . Kim et al.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies suggest that the quantitative measurements obtained from pretreatment PET/CT scans using 68 Ga-DOTA peptides and standard uptake value (SUV) could be indicative of the absorbed dose to the tumor 38,39 or the OARs during PRRT. [40][41][42][43][44][45] Kim et al presented results of a study of 13 patients investigating the predictive value of 68 Ga-DOTATOC PET/CT scan in a linear regression analysis. 44 They presented a general mathematical formula which was based on the mean SUV in 68 Ga-DOTATOC PET/CT scans for predicting the absorbed dose on critical organs (tumor, kidneys, liver, and bone marrow) after treatment with 177 Lu-DOTATATE without assessing any potential relationships between any possible treatment sessions.…”

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confidence: 99%

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A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in ¹⁷⁷Lu‐DOTATATE therapy

Plachouris,

Eleftheriadis,

Nanos

et al. 2023

Medical Physics

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BackgroundStandardized patient‐specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose–effect relationship. Data sets of consistent and reliable inter‐center dosimetry findings are required to characterize this relationship.PurposeWe developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177Lu‐DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients’ imaging data.MethodsPretreatment and posttreatment data for 20 patients with NETs treated with 177Lu‐DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients’ computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects.ResultsWe evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68Ga‐DOTATOC positron emission tomography (PET)/CT and posttherapy 177Lu‐DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68Ga‐DOTATOC PET/CT and any posttherapy 177Lu‐DOTATATE treatment cycle SPECT/CT scans as well as any 177Lu‐DOTATATE SPECT/CT treatment cycle and the consequent 177Lu‐DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from −0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68Ga‐DOTATOC PET/CT and first 177Lu‐DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%–96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet‐based features proved to have high correlated predictive value, whereas non‐linear‐based ML regression algorithms proved to be more capable than the linear‐based of producing precise prediction in our case.ConclusionsThe combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision‐making, especially regarding dose escalation issues.

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Section: Introductionmentioning

confidence: 71%

mentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in ¹⁷⁷Lu‐DOTATATE therapy

Plachouris,

Eleftheriadis,

Nanos

et al. 2023

Medical Physics

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The predictive value of pretherapy [68Ga]Ga-DOTA-TATE PET and biomarkers in [177Lu]Lu-PRRT tumor dosimetry

Akhavanallaf

Peterson

Fitzpatrick

et al. 2023

Eur J Nucl Med Mol Imaging

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Purpose Metastatic neuroendocrine tumors (NETs) overexpressing type 2 somatostatin receptors are the target for peptide receptor radionuclide therapy (PRRT) through the theragnostic pair of 68 Ga/ 177 Lu-DOTATATE. The main purpose of this study was to develop machine learning models to predict therapeutic tumor dose using pre therapy 68 Ga -PET and clinicopathological biomarkers. Methods We retrospectively analyzed 90 segmented metastaticNETs from 25 patients (M14/F11, age 63.7 ± 9.5, range 38-76) treated by 177 Lu-DOTATATE at our institute. Patients underwent both pretherapy [ 68 Ga]Ga-DOTA-TATE PET/CT and four timepoints SPECT/CT at ~ 4, 24, 96, and 168 h post-177 Lu-DOTATATE infusion. Tumors were segmented by a radiologist on baseline CT or MRI and transferred to co-registered PET/CT and SPECT/CT, and normal organs were segmented by deep learning-based method on CT of the PET and SPECT. The SUV metrics and tumor-to-normal tissue SUV ratios (SUV_TNRs) were calculated from 68 Ga -PET at the contour-level. Posttherapy dosimetry was performed based on the co-registration of SPECT/ CTs to generate time-integrated-activity, followed by an in-house Monte Carlo-based absorbed dose estimation. The correlation between delivered 177 Lu Tumor absorbed dose and PET-derived metrics along with baseline clinicopathological biomarkers (such as Creatinine, Chromogranin A and prior therapies) were evaluated. Multiple interpretable machine-learning algorithms were developed to predict tumor dose using these pretherapy information. Model performance on a nested tenfold cross-validation was evaluated in terms of coefficient of determination (R 2 ), mean-absolute-error (MAE), and mean-relative-absolute-error (MRAE). Results SUV mean showed a significant correlation (q-value < 0.05) with absorbed dose (Spearman ρ = 0.64), followed by TLSUV mean (SUV mean of total-lesion-burden) and SUV peak (ρ = 0.45 and 0.41, respectively). The predictive value of PET-SUV mean in estimation of posttherapy absorbed dose was stronger compared to PET-SUV peak , and SUV_TNRs in terms of univariate analysis (R 2 = 0.28 vs. R 2 ≤ 0.12). An optimal trivariate random forest model composed of SUV mean , TLSUV mean , and total liver SUV mean (normal and tumoral liver) provided the best performance in tumor dose prediction with R 2 = 0.64, MAE = 0.73 Gy/GBq, and MRAE = 0.2. Conclusion Our preliminary results demonstrate the feasibility of using baseline PET images for prediction of absorbed dose prior to 177 Lu-PRRT. Machine learning models combining multiple PET-based metrics performed better than using a single SUV value and using other investigated clinicopathological biomarkers. Developing such quantitative models forms the groundwork for the role of 68 Ga -PET not only for the implementation of personalized treatment planning but also for patient stratification in the era of precision medicine.

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177Lu-DOTATATE Theranostics

et al. 2023

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PurposePretreatment predictions of absorbed doses can be especially valuable for patient selection and dosimetry-guided individualization of radiopharmaceutical therapy. Our goal was to build regression models using pretherapy 68Ga-DOTATATE PET uptake data and other baseline clinical factors/biomarkers to predict renal absorbed dose delivered by 177Lu-DOTATATE peptide receptor radionuclide therapy (177Lu-PRRT) for neuroendocrine tumors. We explore the combination of biomarkers and 68Ga PET uptake metrics, hypothesizing that they will improve predictive power over univariable regression.Patients and MethodsPretherapy 68Ga-DOTATATE PET/CTs were analyzed for 25 patients (50 kidneys) who also underwent quantitative 177Lu SPECT/CT imaging at approximately 4, 24, 96, and 168 hours after cycle 1 of 177Lu-PRRT. Kidneys were contoured on the CT of the PET/CT and SPECT/CT using validated deep learning–based tools. Dosimetry was performed by coupling the multi–time point SPECT/CT images with an in-house Monte Carlo code. Pretherapy renal PET SUV metrics, activity concentration per injected activity (Bq/mL/MBq), and other baseline clinical factors/biomarkers were investigated as predictors of the 177Lu SPECT/CT-derived mean absorbed dose per injected activity to the kidneys using univariable and bivariable models. Leave-one-out cross-validation (LOOCV) was used to estimate model performance using root mean squared error and absolute percent error in predicted renal absorbed dose including mean absolute percent error (MAPE) and associated standard deviation (SD).ResultsThe median therapy-delivered renal dose was 0.5 Gy/GBq (range, 0.2–1.0 Gy/GBq). In LOOCV of univariable models, PET uptake (Bq/mL/MBq) performs best with MAPE of 18.0% (SD = 13.3%), and estimated glomerular filtration rate (eGFR) gives an MAPE of 28.5% (SD = 19.2%). Bivariable regression with both PET uptake and eGFR gives LOOCV MAPE of 17.3% (SD = 11.8%), indicating minimal improvement over univariable models.ConclusionsPretherapy 68Ga-DOTATATE PET renal uptake can be used to predict post-177Lu-PRRT SPECT-derived mean absorbed dose to the kidneys with accuracy within 18%, on average. Compared with PET uptake alone, including eGFR in the same model to account for patient-specific kinetics did not improve predictive power. Following further validation of these preliminary findings in an independent cohort, predictions using renal PET uptake can be used in the clinic for patient selection and individualization of treatment before initiating the first cycle of PRRT.

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Differences in tumor-to-normal organ SUV ratios measured with 68Ga-DOTATATE PET compared with 177Lu-DOTATATE SPECT in patients with neuroendocrine tumors

Cited by 5 publications

References 23 publications

A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in ¹⁷⁷Lu‐DOTATATE therapy

A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in ¹⁷⁷Lu‐DOTATATE therapy

The predictive value of pretherapy [68Ga]Ga-DOTA-TATE PET and biomarkers in [177Lu]Lu-PRRT tumor dosimetry

177Lu-DOTATATE Theranostics

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Differences in tumor-to-normal organ SUV ratios measured with 68Ga-DOTATATE PET compared with 177Lu-DOTATATE SPECT in patients with neuroendocrine tumors

Cited by 5 publications

References 23 publications

A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in 177Lu‐DOTATATE therapy

A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in 177Lu‐DOTATATE therapy

The predictive value of pretherapy [68Ga]Ga-DOTA-TATE PET and biomarkers in [177Lu]Lu-PRRT tumor dosimetry

177Lu-DOTATATE Theranostics

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A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in ¹⁷⁷Lu‐DOTATATE therapy

A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in ¹⁷⁷Lu‐DOTATATE therapy