[Effects of miniature free groin perforator flaps in repairing small wounds on hands]

Objective: To investigate the effects of miniature free groin perforator flaps in repairing small wounds on hands. Methods: The retrospective observational study was conducted. Fifteen patients with 16 small wounds on hands were admitted to Tongren Hospital of Wuhan University & Wuhan Third Hospital from July 2020 to October 2022, including 12 males and 3 females, aged 19 to 56 years. The size of skin and soft tissue defect was 2.0 cm×1.5 cm to 6.0 cm×3.0 cm after debridement. According to size and shape of the wounds, 13 single-lobe perforator flaps and 2 bilobed perforator flaps were designed in the groin region, with the flap size of 4.5 cm×2.5 cm to 7.5 cm×3.5 cm. According to the condition of the recipient area, the arteries and veins at the pedicle of the flap were anastomosed to the arteries and veins of the recipient area respectively. The wounds in the donor area of the flap was closed by layered and tension-reducing suture. The thickness of the flap was measured during operation. The survival of the flap was observed, and the complications in the donor and recipient areas were recorded after operation. The appearance and texture of the flap were observed during follow-up. At the last follow-up, the sensory recovery of the affected hand was evaluated, the function of the affected hand was evaluated according to the trial standard of the upper limb partial function evaluation of the Hand Surgery Society of the Chinese Medical Association, the scar in the donor and recipient areas were observed, and the satisfaction of patients for the curative effects was inquired. Results: The thickness of the flap was ranged from 0.3 to 1.0 cm, with an average thickness of 0.6 cm. After operation, 11 single-lobe flaps and 2 bilobed flaps survived well; in the left 2 single-lobe flaps, one flap had venous crisis but returned to normal after removing stitches to reduce tension and bloodletting of flaps, while the other one flap had a little necrosis on tip but healed after dressing change. No complications occurred in donor and recipient areas. During follow-up of 8 to 35 months after operation, the flaps had good elasticity and soft texture; 8 flaps were slightly bloated and were trimmed 3 to 8 months after operation, while the appearances of the other flaps were good. At the last follow-up, all flaps recovered protective feeling; the function of the affected hand was evaluated as excellent in 10 cases, good in 4 cases, and fair in 1 case; only linear scar remained in the donor and recipient areas; the patients were satisfied with the appearance and function recovery of the affected hand. Conclusions: The miniature free groin perforator flaps in repairing small wounds on hands have the advantages of high survival rate of flaps, hidden flap donor area, little damage, few complications, good repair effect, etc., showing clinical application value. It is recommended for repairing non-functional wounds on hands.

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AR, Ristori L, Sexton-Kennedy E, Smith N, Soha A, Spiegel L, Strait J, Taylor L, Tkaczyk S, Tran NV, Uplegger L, Vaandering EW, Zoi I, Avery P, Bourilkov D, Cadamuro L, Chang P, Cherepanov V, Field RD, Koenig E, Kolosova M, Konigsberg J, Korytov A, Kuznetsova E, Lo KH, Matchev K, Menendez N, Mitselmakher G, Muthirakalayil Madhu A, Rawal N, Rosenzweig D, Rosenzweig S, Shi K, Wang J, Wu Z, Adams T, Askew A, Bower N, Habibullah R, Hagopian V, Kolberg T, Martinez G, Prosper H, Viazlo O, Wulansatiti M, Yohay R, Zhang J, Baarmand MM, Butalla S, Elkafrawy T, Hohlmann M, Kumar Verma R, Rahmani M, Yumiceva F, Adams MR, Cavanaugh R, Dittmer S, Evdokimov O, Gerber CE, Hofman DJ, Lemos DS, Merrit AH, Mills C, Oh G, Roy T, Rudrabhatla S, Tonjes MB, Varelas N, Wang X, Ye Z, Yoo J, Alhusseini M, Dilsiz K, Emediato L, Karaman G, Köseyan OK, Merlo JP, Mestvirishvili A, Nachtman J, Neogi O, Ogul H, Onel Y, Penzo A, Snyder C, Tiras E, Amram O, Blumenfeld B, Corcodilos L, Davis J, Gritsan AV, Kyriacou S, Maksimovic P, Roskes J, Sekhar S, Swartz M, Vámi T, Abreu A, Alcerro Alcerro LF, Anguiano J, Baringer P, Bean A, Flowers Z, King J, Krintiras G, Lazarovits M, Le Mahieu C, Lindsey C, Marquez J, Minafra N, Murray M, Nickel M, Rogan C, Royon C, Salvatico R, Sanders S, Smith C, Wang Q, Wilson G, Allmond B, Duric S, Ivanov A, Kaadze K, Kalogeropoulos A, Kim D, Maravin Y, Mitchell T, Modak A, Nam K, Roy D, Rebassoo F, Wright D, Adams E, Baden A, Baron O, Belloni A, Bethani A, Eno SC, Hadley NJ, Jabeen S, Kellogg RG, Koeth T, Lai Y, Lascio S, Mignerey AC, Nabili S, Palmer C, Papageorgakis C, Wang L, Wong K, Busza W, Cali IA, Chen Y, D’Alfonso M, Eysermans J, Freer C, Gomez-Ceballos G, Goncharov M, Harris P, Kovalskyi D, Krupa J, Lee YJ, Long K, Mironov C, Paus C, Rankin D, Roland C, Roland G, Shi Z, Stephans GSF, Wang J, Wang Z, Wyslouch B, Yang TJ, Chatterjee RM, Crossman B, Hiltbrand J, Joshi BM, Kapsiak C, Krohn M, Kubota Y, Mahon D, Mans J, Revering M, Rusack R, Saradhy R, Schroeder N, Strobbe N, Wadud MA, Cremaldi LM, Bloom K, Bryson M, Claes DR, Fangmeier C, Finco L, Golf F, Joo C, Kamalieddin R, Kravchenko I, Reed I, Siado JE, Snow GR, Tabb W, Wightman A, Yan F, Zecchinelli AG, Agarwal G, Bandyopadhyay H, Hay L, Iashvili I, Kharchilava A, McLean C, Morris M, Nguyen D, Pekkanen J, Rappoccio S, Williams A, Alverson G, Barberis E, Haddad Y, Han Y, Krishna A, Li J, Lidrych J, Madigan G, Marzocchi B, Morse DM, Nguyen V, Orimoto T, Parker A, Skinnari L, Tishelman-Charny A, Wamorkar T, Wang B, Wisecarver A, Wood D, Bhattacharya S, Bueghly J, Chen Z, Gilbert A, Hahn KA, Liu Y, Odell N, Schmitt MH, Velasco M, Band R, Bucci R, Cremonesi M, Das A, Goldouzian R, Hildreth M, Hurtado Anampa K, Jessop C, Lannon K, Lawrence J, Loukas N, Lutton L, Mariano J, Marinelli N, Mcalister I, McCauley T, Mcgrady C, Mohrman K, Moore C, Musienko Y, Ruchti R, Townsend A, Wayne M, Yockey H, Zarucki M, Zygala L, Bylsma B, Carrigan M, Durkin LS, Hill C, Joyce M, Lesauvage A, Nunez Ornelas M, Wei K, Winer BL, Yates BR, Addesa FM, Das P, Dezoort G, Elmer P, Frankenthal A, Greenberg B, Haubrich N, Higginbotham S, Kopp G, Kwan S, Lange D, Loeliger A, Marlow D, Ojalvo I, Olsen J, Stickland D, Tully C, Malik S, Bakshi AS, Barnes VE, Chawla R, Das S, Gutay L, Jones M, Jung AW, Kondratyev D, Koshy AM, Liu M, Negro G, Neumeister N, Paspalaki G, Piperov S, Purohit A, Schulte JF, Stojanovic M, Thieman J, Virdi AK, Wang F, Xiao R, Xie W, Dolen J, Parashar N, Acosta D, Baty A, Carnahan T, Dildick S, Ecklund KM, Fernández Manteca PJ, Freed S, Gardner P, Geurts FJM, Kumar A, Li W, Padley BP, Redjimi R, Rotter J, Yang S, Yigitbasi E, Zhang Y, Bodek A, de Barbaro P, Demina R, Dulemba JL, Fallon C, Garcia-Bellido A, Hindrichs O, Khukhunaishvili A, Parygin P, Popova E, Taus R, Van Onsem GP, Goulianos K, Chiarito B, Chou JP, Gershtein Y, Halkiadakis E, Hart A, Heindl M, Jaroslawski D, Karacheban O, Laflotte I, Lath A, Montalvo R, Nash K, Osherson M, Routray H, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Wang H, Acharya H, Delannoy AG, Fiorendi S, Holmes T, Nibigira E, Spanier S, Bouhali O, Dalchenko M, Delgado A, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Malhotra S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Lamichhane K, Lee SW, Mengke T, Muthumuni S, Peltola T, Volobouev I, Whitbeck A, Appelt E, Greene S, Gurrola A, Johns W, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Cummings G, Hakala J, Hirosky R, Ledovskoy A, Li A, Neu C, Perez Lara CE, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Madhusudanan Sreekala J, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Bunichev V, Chadeeva M, Chekhovsky V, Danilov M, Dermenev A, Dimova T, Dremin I, Dubinin M, Dudko L, Epshteyn V, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Ivanov Y, Kachanov V, Kardapoltsev L, Karjavine V, Karneyeu A, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Klyukhin V, Kodolova O, Konstantinov D, Korenkov V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Litomin A, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oskin A, Ovtin I, Palichik V, Perelygin V, Perfilov M, Petrushanko S, Popov V, Radchenko O, Rusinov V, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. A search for decays of the Higgs boson to invisible particles in events with a top-antitop quark pair or a vector boson in proton-proton collisions at s=13TeV

A search for decays to invisible particles of Higgs bosons produced in association with a top-antitop quark pair or a vector boson, which both decay to a fully hadronic final state, has been performed using proton-proton collision data collected at s = 13 Te V by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138fb - 1 . The 95% confidence level upper limit set on the branching fraction of the 125Ge V Higgs boson to invisible particles, B ( H → inv ) , is 0.54 (0.39 expected), assuming standard model production cross sections. The results of this analysis are combined with previous B ( H → inv ) searches carried out at s = 7 , 8, and 13Te V in complementary production modes. The combined upper limit at 95% confidence level on B ( H → inv ) is 0.15 (0.08 expected).

Observation of τ Lepton Pair Production in Ultraperipheral Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV

We present an observation of photon-photon production of τ lepton pairs in ultraperipheral lead-lead collisions. The measurement is based on a data sample with an integrated luminosity of 404  μb^{-1} collected by the CMS experiment at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02  TeV. The γγ→τ^{+}τ^{-} process is observed for τ^{+}τ^{-} events with a muon and three charged hadrons in the final state. The measured fiducial cross section is σ(γγ→τ^{+}τ^{-})=4.8±0.6(stat)±0.5(syst)  μb, where the second (third) term corresponds to the statistical (systematic) uncertainty in σ(γγ→τ^{+}τ^{-}) in agreement with leading-order QED predictions. Using σ(γγ→τ^{+}τ^{-}), we estimate a model-dependent value of the anomalous magnetic moment of the τ lepton of a_{τ}=0.001_{-0.089}^{+0.055}.

[A case of left pulmonary artery sling combined with congenital tracheal stenosis in an adult]

Pulmonary artery sling in adults is a rare congenital vascular malformation usually accompanied by tracheal and bronchial stenosis. Due to its high mortality risk and relatively poor prognosis, it has rarely been reported in adults. We reported a middle-aged patient who presented with shortness of breath, predominantly after activity, since childhood. He was diagnosed with "tracheal stenosis" in another hospital and received symptomatic treatment. The diagnosis of left pulmonary artery sling with congenital tracheal stenosis was confirmed by multi-slice spiral CT (MSCT), airway examination with flexible bronchoscope and 3D image post-processing system. Data from this case and the related literatures have been summarized and analyzed. This will help clinicians to improve their level of diagnosis and treatment.

[Differential expression of LLGL2 in prostate ductal adenocarcinoma and acinar adenocarcinoma and its significance]

Objective: To investigate the expression differences of LLGL2 between prostatic ductal adenocarcinoma (PDA) and prostatic acinar adenocarcinoma, and its potential clinical significance. Methods: Eighteen patients diagnosed of PDA or prostatic acinar adenocarcinoma with PDA component by histopathology during January 2015 and December 2019 in the Beijing Hospital, China were retrospectively studied. The transcriptome analysis was conducted using the tissue of PDA and prostatic acinar adenocarcinoma. Differentially expressed genes and the differences in expression profiles were identified. Further, differentially expressed proteins were verified by immunohistochemistry. Results: The tissue from 8 of the 18 patients were used for transcriptome analysis, the results of which were compared with data from public databases. 129 differentially expressed genes were identified. 45 of them were upregulated while 84 were downregulated. The results of gene enrichment analysis and gene oncology (GO) analysis revealed that the differentially expressed genes were mostly enriched in the hypertrophic cardiomyopathy and interleukin-17 related pathways. GPAT2, LLGL2, MAMDC4, PCSK9 and SMIM6 were differentially expressed between PDA and prostatic acinar adenocarcinoma. Moreover, LLGL2 was more likely expressed in the cytoplasm (P=0.04) than the nucleus (P<0.01) in PDA, compared with prostatic acinar adenocarcinoma. Conclusions: The gene expression profiling indicates that PDA are very similar to prostatic acinar adenocarcinoma. Among the differentially expressed proteins screened and verified in this study, the expression of GPAT2, LLGL2, MAMDC4 and PCSK9 is increased in PDA, while that of SMIM6 is reduced in PDA. The expression of LLGL2 shows significantly different patterns between PDA and prostatic acinar carcinoma, and thus may help differentiate PDA from prostatic acinar adenocarcinoma in clinical practice.

[Development of auxiliary early predicting model for human brucellosis using machine learning algorithm]

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.

A Comparison of Clinicopathologic Outcomes and Patterns of Lymphatic Spread across Neoadjuvant Chemotherapy, Neoadjuvant Chemoradiotherapy and Neoadjuvant Immunochemotherapy in Locally Advanced Esophageal Squamous Cell Carcinoma

PURPOSE/OBJECTIVE(S)

To evaluate the differences in pathologic complete response (pCR) rates, TRG score, pathologic T stage and the pattern of lymphatic spread among patients receiving neoadjuvant chemotherapy (NCT) or neoadjuvant chemoradiotherapy (NCRT) or neoadjuvant immunochemotherapy (NICT) prior to esophagectomy for locally advanced esophageal squamous cell carcinoma (ESCC).

MATERIALS/METHODS

A total of 702 patients with ESCC who completed transthoracic esophagectomy followed neoadjuvant therapy at three cancer centers from January 2017 to December 2022 were enrolled. Among the included patients, 382 patients were treated with NCR, 172 with NCRT, and 148 with NICT. Inverse probability of treatment weighting (IPTW) was performed to control potential confounding factors. Pathological response of primary tumor was evaluated using the Chirieac modified tumor regression grade (TRG) system. The complete regression of primary lesion and nodal metastases were considered pCR. Lymph node classification system used the 8th edition of AJCC. Specimens were assessed for pattern of lymphatic spread.

RESULTS

After adjusting for baseline characteristics, the R0 resection rate did not significantly differ between the patients receiving NCT or NCRT or NICT (99.48% vs.100% vs.98.65%, P = 0.273). Compared with the NCT group, the NCRT group and NICT group had an advantage in pathological response (P<0.05). The pCR rate was 7.07% in the NCT group, 30.23% in the NCRT group, and 22.30% in the NICT group. Compared to the other two groups, the TRG score (P<0.05) and pathologic T stage (P<0.05) in the NCT group were significantly higher. In the NCT group, 9.97% had ypT0 disease, compared with 35.76% in the NCRT group and 25.68% in the NICT group. And in the NCT group, 9.71% had TRG1 disease, compared with 32.76% in the NCRT group and 25% in the NICT group. Compared with NICT, NCRT can significantly reduce the rate of LNM in station 1R (0 vs 3.38%, P<0.05) and 2R (1.15% vs 6.76%, P<0.05). Subgroup analysis according to the tumor location distribution showed that in upper thoracic cases, there was no statistical difference in LNM rates among stations no matter whether patients received NCT or NCRT or NICT. NICT group had higher LNM rates in station 2R (9.1%) in middle thoracic cases (P<0.05) and in station 18 (7.5%) (P<0.05) in lower thoracic cases, compared with the NCRT group and NCT group.

CONCLUSION

NCRT or NICT followed by surgery may result in a promising pCR rate and show a better performance in therapeutic response of primary lesion. No matter whether patients received NCT or NCRT or NICT, multiple level and skip node metastases are common, and adequate lymphadenectomy should be achieved to ensure the complete removal of metastatic lymph nodes.

Identification and Automated Delineation of Radioresistant Biological Tumor Volume in Nasopharyngeal Carcinoma Based on Magnetic Resonance Imaging Radiomics

PURPOSE/OBJECTIVE(S)

Widespread use of intensity modulated radiotherapy (IMRT) has improved the tumor control rate of nasopharyngeal carcinoma (NPC). However, nearly 20% of the patients with local-advanced NPC would relapse after precise irradiation and 80% of the recurrent lesions occur within the high dose field, suggesting that there are radiation-resistant cancer cell subsets within the tumor. In this context, identification and contouring of radiation resistance region of NPC for dose escalation at primary IMRT could be advantageous. In this work, we proposed a two-step radiomics workflow to predict local relapse and the recurrent region of NPC before primary IMRT.

MATERIALS/METHODS

In this single-center, retrospective study, pre-treatment magnetic resonance (MR) sequences of T1-weighted imaging (T1-w) and contrast-enhanced T1-weighted imaging (CET1-w) were collected from 800 patients of newly diagnosed and non-metastatic NPC between April 2009 and December 2015. The primary gross tumor volume (GTVp) of all patients and the actual recurrent lesion (GTVr) of patients who suffered from local recurrence were manually contoured for further analysis. A two-step complete radiomics workflow was designed to predict tumor recurrence and segment the region. First, least absolute shrinkage and selection operator (LASSO) was utilized for radiomics features selection of GTVp and support vector machine (SVM) was adopted to predict the recurrence. If the model predicts a recurrence, then the workflow utilizes an improved 3D U-Net to segment the recurrent region. Area under receiver operating characteristic curve (ROC-AUC) was used to evaluate the performance of tumor recurrence prediction, and Dice similarity coefficient (DSC) was used to assess the consistence between the actual and predicted GTVr.

RESULTS

Of 800 NPC patients, 95 (11.9%) patients developed in-field local recurrence. For recurrence risk prediction, the SVM ensemble model (T1-w+CET1-w) was selected for further application with higher sensitivity. The average ROC-AUC, specificity, sensitivity of the SVM ensemble model in a 5-fold cross-validation and in the independent test set of 160 patients were 0.922, 0.922, 0.777 and 0.928, 0.915, 0.737, respectively. Moreover, for recurrent region segmentation, the multi-modality (T1-w+CET1-w) model was superior to the single-modality (T1-w or CET1-w) model. In an independent test set of 15 patients, the DSC, sensitivity and 95% Hausdorff Distance between actual and predicted GTVr was 0.549±0.176, 0.696±0.118 and 9.813±4.788 which was superior to 0.444±0.188, 0.497±0.218 and 12.047±5.361 of original 3D U-Net.

CONCLUSION

The proposed two-step radiomics workflow showed a good performance in predicting tumor recurrence of NPC. The predicted location of the recurrence lesion was all accurate, but there was still a certain difference between the volume of the automated delineated and actual GTVr, which needed to be further optimized to be used as biological tumor volume.

Towards Clinical Proton Minibeam Radiation Therapy (pMBRT): Development of Clinical pMBRT System Prototype and pMBRT-Specific Treatment Planning Method

PURPOSE/OBJECTIVE(S)

Proton minibeam radiation therapy (pMBRT) is an emerging spatially fractionated RT (SFRT) modality that can provide very high therapeutic index compared to conventional radiotherapy methods and clinically-available SFRT methods (GRID and LATTICE). The biological data collected thus far encourage the preparation of clinical trials in pMBRT. This work is to facilitate the clinical translation of pMBRT by developing (1) the first clinical pMBRT system prototype worldwide, readily available for both small-animal biology studies and large-animal pMBRT trials; (2) pMBRT-specific treatment planning method with peak-valley dose ratio (PVDR) optimization capability for large animal and patient pMBRT trials, which is currently unavailable.

MATERIALS/METHODS

The pMBRT system is based on clinically-used pencil-beam-scanning proton machine equipped with large-field clinical-size pMBRT collimator, pMBRT-dedicated treatment planning system, and KV/CBCT imaging guidance. The multi-slit brass collimator has 10 × 10 cm field size, 0.4mm width per slit and 4 mm center-to-center distance. The divergence of slits is tailed to the divergence of the proton beam. A unique universal collimator design is implemented, so that we can keep the outer fitting to the snout and conveniently inter-change collimators as needed. The pMBRT-specific treatment planning method jointly optimizes PVDR and dose objectives, to meet a minimal PVDR threshold and maximize PVDR, to avoid the situations where meeting dose objectives can compromise PVDR when PVDR were not optimized. In addition, the survival fraction for organs at risk is also optimized. The dose calculation engine is based on the Monte Carlo method using TOPAS. The optimization algorithm utilizes total variation and L1 sparsity regularization to maximize PVDR and iterative convex relaxation method to solve the optimization problem.

RESULTS

Monte Carlo simulations via TOPAS were performed to design this large-field multi-slit collimator, using the beam structure and the beam data specific to our proton system, with mean dose rate of 8 Gy/min under clinical condition with the collimator in place. The feasibility of using this pMBRT system for small-animal studies has been demonstrated, with customized 3D printed holder for immobilizing small animals, on-board KV imaging system for accurate small-animal positioning, and the GAFchromic film for verifying radiation dose and PVDR. On the other hand, the efficacy of pMBRT-specific treatment planning method (with PVDR optimization capability) to improve PVDR has been demonstrated using retrospective patient planning studies in comparison with standard proton treatment planning method (without PVDR optimization capability).

CONCLUSION

The initial development of a clinical pMBRT system prototype and pMBRT-specific treatment planning method of PVDR optimization capability has been completed with ongoing efforts to make this system ready for large-animal pMBRT studies.

An inverse Jiles-Atherton model of nanocrystalline magnetic core for nanoseconds square pulsed magnetization

The magnetic core is a key component of a linear transformer driver (LTD), and the accuracy of the core model affects the calculation of the LTD power flow and the prediction of the output waveform. In this paper, a magnetization model based on the inverse Jiles-Atherton (inverse J-A) model is developed and a particle swarm algorithm is used to identify the parameters and to obtain the variation of the parameters with the excitation characteristic. A nanoseconds square wave LTD magnetic core test platform was built to obtain the magnetization characteristics of nanocrystalline magnetic cores under different excitation characteristic parameters. Under square wave pulses, due to the presence of harmonic components, core loss is more complex. In view of the fitting deviation caused by the traditional J-A model not considering harmonic factors and anisotropy, a dynamic loss correction factor is proposed. Through a comparison of experimental and simulation results, this model can well reflect the magnetization process and has high accuracy in fitting dynamic hysteresis loops and predicting losses, which is important for guiding the design of a square pulse LTD.

SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data

PURPOSE/OBJECTIVE(S)

Limited-angle CBCT (LA-CBCT) is of great clinical interest, because the scanning time and the patient dose are proportional to the scanning range of gantry rotation angles of CBCT. However, the image reconstruction for LA-CBCT remains technically challenging, which suffers from severe wedge artifacts and image distortions. This work aims to improve LA-CBCT by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge.

MATERIALS/METHODS

Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration.

RESULTS

SEA-Net was validated in comparison with analytic (FDK), iterative (TV), image-domain DL (DDNet and FED-INet, data-domain DL (DCAR), dual-domain DL (Sam'Net), and various unrolling DL (hdNet, CTNet, FSR-Net, CasRedSCAN) methods. Among all methods, the SEA-Net had the best image reconstruction quality as quantified by root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), for various LA-CBCT problems of 90°-180° projection data. In addition, LA-CBCT via SEA-Net provided comparable accuracy for both patient setup (quantified by image registration accuracy from planning CT (pCT) to CBCT) and dose calculation (see the table), with full-view CBCT.

CONCLUSION

We explored various DL methods and developed an image-domain-based method termed SEA-Net that provided the best image quality for clinical projection data. To the best of our knowledge, this is the first feasibility study of the real clinical-projection-data-based LA-CBCT. Moreover, LA-CBCT via SEA-Net can potentially provide comparable accuracy for patient setup and dose calculation, with full-view CBCT.

Induction Immunotherapy vs. Consolidation Immunotherapy for Unresectable Stage III NSCLC

PURPOSE/OBJECTIVE(S)

Consolidation immunotherapy after chemoradiotherapy (CRT) is the standard of care for unresectable stage III non-small cell lung cancer (NSCLC). However, whether upfront immunotherapy before CRT has similar benefits has not been addressed. This study aimed at exploring the efficacy and safety of induction immunotherapy for unresectable stage III NSCLC through real-world data.

MATERIALS/METHODS

Patients diagnosed with stage III NSCLC who received immunotherapy in combination with sequential (sCRT) or concurrent CRT (cCRT) between November 2018 and December 2021 were retrospectively identified. Patients were divided into induction (Ind), consolidation (Con) and induction plus consolidation (Ind+Con) immunotherapy groups. Progression-free survival (PFS) and overall survival (OS) were assessed from the initiation of treatment and estimated by Kaplan‒Meier method. The potential factors affecting PFS and OS were analyzed by univariate and multivariate Cox regression models.

RESULTS

One hundred and two patients were included, with 52 (51.0%) patients in the Ind group, 35 (34.3%) in the Con group and 15 (14.7%) in the Ind+Con group. Median PFS was 24.0 months vs. 36.0 months vs. 19.0 months in the three groups, and 2-year PFS were 43.0% vs 51.1% vs 44.4% (p = 0.940). Median OS was not reached (NR) vs. 44.0 months vs. NR, with a 2-year OS rate of 80.5% vs. 84.4% vs. 86.2% (p = 0.861). In the cCRT setting, 2-year PFS rates were 56.7% vs. 71.6% vs. 100.0% (p = 0.439), 2-year OS rates were 92.3% vs. 89.3% vs. 100.0% in the three groups (p = 0.827). In multivariate analysis, elder (HR = 0.487, p = 0.037) and cCRT (HR = 0.282, p = 0.001) were the independent factors favoring PFS, while only elder (HR = 0.088, p = 0.021) was the independent factors favoring OS. Adverse events were similar in the three arms. Further analysis found the objective response rate (ORR) and disease control rate (DCR) in the Ind and Ind+Con group after induction immunotherapy were 59.7% and 98.5%, respectively. Only 1 (1.5%) patient developed progression. Subgroup analysis showed no significant difference in PFS (p = 0.520) and OS (p = 0.116) between patients who responded to induction immunotherapy (PR+CR) and those who did not (SD+PD). Patients with <4 cycles of induction immunotherapy exhibited numerically better PFS than those with ≥4 cycles of induction immunotherapy (p = 0.113) and improved OS (p = 0.021).

CONCLUSION

Induction immunotherapy may achieve similar survival benefits to consolidation immunotherapy, and the combination of induction and consolidation immunotherapy with cCRT appears to achieve better outcomes. It seems feasible and safe to upfront immunotherapy before CRT, and further investigations on the combination of induction immunotherapy and CRT are warranted.

Radical Chemoradiotherapy vs. Radical Surgery plus Adjuvant Chemotherapy or Chemoradiotherapy in Locally Advanced Primary Small Cell Carcinoma of the Esophagus: A Multicenter Retrospective Study in China

PURPOSE/OBJECTIVE(S)

The purpose was to compare the treatment outcomes of radical chemoradiotherapy versus radical surgery plus adjuvant chemotherapy or chemoradiotherapy in locally advanced primary small cell carcinoma of the esophagus (LA-PSCCE). The hypothesis was that radical chemoradiotherapy had better overall survival (OS) than radical surgery plus adjuvant chemotherapy or chemoradiotherapy.

MATERIALS/METHODS

This large-scale multicenter retrospective study in China enrolled patients with newly diagnosed LA-PSCCE (T3-4N0M0 or TanyN+M0, AJCC 8th edition) from 2008 to 2021. According to different curative treatment approaches, patients were divided into two groups: radical chemoradiotherapy (group: CRT), and radical surgery following adjuvant chemotherapy or chemoradiotherapy (group: S + CT/CRT). The propensity score match (PSM) was applied to reduce the effect of confounding biases in clinicopathological characteristics (age, gender, KPS, tumor location, tumor length, and cTNM stage). Univariate Cox-regression analysis and Kaplan-Meier curve were calculated for OS. Statistical results were summarized as hazard ratio (HR), 95% confidence interval (CI) and P value. A two-sided P < 0.05 was regarded to be statistically significant.

RESULTS

A total of 291 patients with a median follow-up of 4.3 years were retrospectively enrolled. After PSM analysis, 94 and 94 patients were eventually included in group CRT and S + CT/CRT, respectively. Group CRT demonstrated a significantly superior survival than group S + CT/CRT (HR, 0.63; 95% CI, 0.43-0.91; P = 0.01), with a 3-year OS of 49.5% and 27.8% (P = 0.02), respectively. In secondary analysis, patients treated with radical chemoradiotherapy consistently showed significant survival benefit than those with radical surgery plus adjuvant chemoradiotherapy (HR, 0.4; 95% CI, 0.21-0.79; P = 0.008).

CONCLUSION

For patients with newly diagnosed LA-PSCCE, radical chemoradiotherapy should be a preferred recommendation in real-world clinical practice.

Deep Learning for Automated Contouring of Primary Gross Tumor Volumes by MRI for Radiation Therapy of Brain Metastasis

PURPOSE/OBJECTIVE(S)

Radiotherapy is one of the most effective methods for the treatment of brain metastases (BMs). Traditional manual delineation of primary gross tumor volumes (GTV) of multiple BMs (especially small metastases) in radiotherapy practice is extremely labor intensive and highly dependent on oncologists' experience, achieving the precise and efficient automatic delineation of BMs is of great significance for efficient and homogeneous one-stop adaptive radiotherapy.

MATERIALS/METHODS

We retrospectively collected 62 MRI (non-enhanced T1-weighted sequences) sequences of 50 patients with BMs from January 2020 to July 2021. An automatic model (BUC-Net) for automatic delineation BMs was proposed in this work, which was based on deep learning by combining 3D bottler layer module and the cascade architecture to improve the accuracy and efficient of BMs' automatic delineation, especially for small metastases with tiny size and relatively low contrast. The prosed method was compared with the existing 3D U-Net (U-Net) and 3D U-Net Cascade (U-Net Cascade). The performance of our proposed method was evaluated by Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) with human experts.

RESULTS

The automatic segmentation results of BUC-Net evaluated with 310 BMs in 13 test patients was summarized in Table 1. These BMs in each test patient were automatically delineated by two types of contours: as a whole tumor contour (Whole-delineation) and the multiple tumor contours (Multiple-delineation). BUC-Net performed the best mean DSC and HD95, which is significantly outperformed U-Net (Whole-delineation: 0.911 & 0.894 of DSC, Multiple-delineation: 0.794 & 0.754 of DSC, P < 0.05 for both) and U-Net cascade (Whole-delineation: 0.947 & 7.141 of HD95, Multiple-delineation: 0.902 & 1.171 of HD95, P < 0.05 for both); Additionally, BUC-Net achieved the best mean ASD for Whole-delineation and comparable ASD (0.290 & 0.277, P > 0) for Multiple-delineation with U-Net Cascade.

CONCLUSION

Our results showed that the proposed approach is promising for the automatic delineation of BMs in MRI, which can be integrated into a radiotherapy workflow to significantly shorten segmentation time.

Detection of Circulating Tumor DNA Correlates with Recurrence and Survival in Localized Non-Small-Cell Lung Cancer: A Meta-Analysis

PURPOSE/OBJECTIVE(S)

Circulating tumor DNA (ctDNA) is approved recently to use in clinical practice of solid tumor. Several large-scale prospective studies also revealed that minimal residual disease based on ctDNA (ctDNA-MRD) is a potential predictive and prognostic biomarker of localized non-small-cell lung cancer (NSCLC) receiving curative treatment (surgery or radiotherapy). However, there are still barriers to clinical management of ctDNA/ctDNA-MRD in localized NSCLC, and the most significant is effectiveness and detection times of ctDNA/ctDNA-MRD. This meta-analysis aims to clarify the prognostic value of the ctDNA and ctDNA-MRD in predicting the disease recurrence and survival of localized NSCLC.

MATERIALS/METHODS

Electronic databases (Pubmed/MEDLINE, Web of Science, Cochrane Library, meeting abstracts) were comprehensively searched for eligible studies from January 2001 to January 2023. The Hazard ratio (HR) from relevant reports was extracted to better evaluate the correlation of ctDNA and ctDNA-MRD detected in plasma with clinical outcomes among patients with localized NSCLC. Pooled results including ctDNA detection rate, disease-/relapse-/progression- free survival (DFS/RFS/PFS) and overall survival (OS) were obtained and analyzed by Review Manager 5.4.1.

RESULTS

A total of 18 eligible studies with 2692 patients were enrolled in the final analysis. The pooled analysis revealed that ctDNA detection in pretreatment plasma indicated poor prognosis for DFS/RFS/PFS (HR 3.82, 95% CI 2.85 - 5.12, p < .00001; Figure 1) with a long-term effect on OS (HR 4.88, 95% CI 3.29 - 7.24, p < .00001; Figure 2). The same result was also observed in patients with positive posttreatment ctDNA-MRD who have shorter DFS/RFS/PFS (HR 7.15, 95% CI 5.50 - 9.31, p < .00001; Figure 3) and OS (HR 4.34, 95% CI: 2.51-7.51, p < .00001; Figure 4) compared to negative groups.

CONCLUSION

Based on the results from our meta-analysis, the presence of pretreatment ctDNA or posttreatment ctDNA-MRD in radically treated localized NSCLC is associated with higher risk of recurrence and poorer survival. This finding is supportive of ctDNA/ctDNA-MRD becoming a widespread prognostic biomarker in localized NSCLC.

Deep Learning and GAN-Synthesis for Auto-Segmentation of Pancreatic Cancer by Non-Enhanced CT for Adaptive Radiotherapy

PURPOSE/OBJECTIVE(S)

In conventional adaptive radiotherapy (ART) for pancreatic cancer, contrast-enhanced CT (CECT) helps to more precisely delineate primary gross tumor volume (GTV) than non-enhanced CT (NECT). However, frequent use of contrast medium can damage kidneys and prolong treatment time. Moreover, traditional manual delineation is labor-intensive and highly dependent on the experience of oncologists. Currently, automatic delineation based on deep learning with Generative Adversarial Networks (GAN)-based CT synthesis is one of the most feasible solutions to these problems.

MATERIALS/METHODS

A dataset of 35 pancreatic cancer patients was retrospectively collected from May 2021 to December 2022. All patients consist of a pair of NECT and CECT. We designed and developed an automatic delineation framework (Proposed) for GTV of pancreatic cancer based on Trans-cycleGAN and a modified 3D U-Net. TranscycleGAN can not only synthesize CECT from NECT, but can also augment the amount of CT images; then all real and synthesized CT images were used to train the modified 3D U-Net for automatic delineation of GTV; finally, our framework was able to automatically delineate GTV by NECT, but not only by CECT. Our framework was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with oncologists' manual delineation ("gold standard").

RESULTS

The evaluation results were summarized in Table 1. The proposed framework achieved the best automatic delineation results by NECT, which was superior to that of CECT: 0.917 & 0.903 of DSC, 2.498mm & 3.029mm of HD95, 0.481mm & 0.534mm of ASD, p < 0.05 for DSC and HD95. Specifically, it is significantly superior to the automatic delineation results using U-Net by CECT 0.917 & 0.818 of DSC, 2.498mm & 13.228mm of HD95, 0.481mm & 3.633mm of ASD, p < 0.05 for DSC.

CONCLUSION

We proposed an automatic delineation framework for contouring GTV in ART of pancreatic cancer based on deep learning and Trans-cycleGAN network. This framework could automatically delineate GTV and achieve better performance with NECT compared to CECT. Our method could not only reduce the use of contrast medium, but also increase the precision and effectiveness of tumor delineation, which could have a positive impact on precision radiotherapy.

All-in-One Online Radiotherapy for Nasopharyngeal Carcinoma: Preliminary Results of Treatment Time, Contouring Accuracy, Treatment Plan Quality and Patient Compliance

PURPOSE/OBJECTIVE(S)

To explore the feasibility of Fan-beam CT (FBCT)-based all in one (AIO) online workflow for nasopharyngeal carcinoma (NPC) in radical radiotherapy setting, and to preliminarily describe the timing of different steps in the process, contouring accuracy of regions of interest (ROIs), target coverage, organs at risk (OARs) dose and patient compliance.

MATERIALS/METHODS

From March 16, 2022 to January 04, 2023, 25 NPC patients (22/25 diagnosed as phase III/IV disease according to 8th edition of the AJCC/UICC staging system) consecutively treated with AIO radiotherapy were prospectively enrolled. All patients received mask fixation and MRI simulation scan in advance. Primary gross tumor volume (GTVp) of nasopharynx was automatically delineated by AI and edited manually on MRI images. AIO online workflow started with an integrated KV-level CT in a CT-integrated linear accelerator. After that GTVp was registrated to CT images and other ROIs was contoured automatically and then modified manually as needed. Subsequently automatic treatment plan was calculated and optimized until the dose of target and OARs was evaluated satisfactory by physicians and physicists. Finally, treatment was delivered using volumetric modulated arc treatment (VMAT), with prescribed dose of 6996 cGy/ 33 fractions to the GTVp.

RESULTS

Twenty-four patients (24/25, 96%) completed the AIO radiotherapy workflow successfully, with average treatment time of 28.3 min (range: 19.9-42.4 min). the AI-assisted ROIs automatically contouring took 1.55 min in average (range: 1.32-1.77 min), with an average DICE of 97.7% compared with modified contouring, and the average DICE was 95.7% for clinical tumor volume 1 (CTV1), 88.6% for CTV2, 73.6% for GTVn (cervical lymph node), 99.3% for 30 OARs. The automatic treatment plan averagely needed 3.5 min, and the pass rate of radiotherapy planning was 91.7% (22/24). The target coverage for PTVs for GTVp, CTV1, and CTV2 was 99.3%, 99.8%, 98.0% respectively. As for the dose of OARs, the average Dmax of brainstem was 5,583cGy; the Dmax of spinal cord was 3,467cGy; the Dmean of parotid was 3,285 cGy. The average monitor units of all patients was 643 MU and the delivery took 2.93 min. Patient compliance with respect to AIO workflow and total treatment time was excellent.

CONCLUSION

The AIO online radiotherapy was promising for NPC patients, with clinically acceptable AI assisted ROIs contouring and treatment planning, as well as favorable patient compliance to the AIO online workflow.

A Machine Learning Method to Predict Pathological Complete Response of Esophageal Cancer after Neoadjuvant Chemoradiotherapy with Clinicohematological Markers and MR Radiomics: A Multi-Center Study

PURPOSE/OBJECTIVE(S)

Nearly 30% of patients with local advanced esophageal cancer achieved pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who may benefit from organ-preservation strategy under accurate prediction of pCR. We aimed to develop and validate machine learning models based on clinicohematological markers and MR radiomics to accurately predict pCR of esophageal cancer after nCRT.

MATERIALS/METHODS

In this multi-center study, eligible patients with esophageal cancer who received baseline MR scan (T2-weighted image) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Pre-nCRT and post-nCRT blood test results were collected to calculate hematological markers. Models were constructed by machine learning based on clinicohematological markers and MR radiomics to predict pCR. Area under the curve (AUC) and cut-off analysis were used to evaluate model performances.

RESULTS

Totally 154 patients (81 in the training set and 73 in the testing set) were enrolled. The combined model integrating pre-nCRT monocyte-to-lymphocyte ratio and 6 radiomics features achieved AUC of 0.800 (95% CI 0.671-0.918) in the testing set, with sensitivity of 79.2% (95% CI 62.5%-95.8%), specificity of 83.7% (95% CI 73.5%-93.9%), positive predictive value of 76.0% (95% CI 62.5%-90.0%), and negative predictive value of 89.6% (95% CI 82.0%-95.8%).

CONCLUSION

A machine learning model based on clinicohematological markers and MR radiomics to predict pCR after nCRT for patients with esophageal cancer was developed and validated, providing a novel tool for personalized treatment. It is necessary to further validate in more large datasets.

Automated Contouring of Cervical Lymph Nodes and Clinical Target Volumes for Nasopharyngeal Carcinoma Based on Deep Learning and Experience Constraints

PURPOSE/OBJECTIVE(S)

Application of artificial intelligence (AI) for automated contouring of tumor volumes and organs at risk (OARs) for radiotherapy of nasopharyngeal carcinoma (NPC) leads to improved contouring accuracy and efficiency. However, few studies have involved the automated contouring of gross tumor volume of cervical lymph nodes (GTVn) and clinical target volumes (CTVs). In this work, we proposed an AI automated contouring tool for GTVn and CTVs for radiotherapy of NPC on the plain scans of planning compute tomography (CT).

MATERIALS/METHODS

In this retrospective study, plain scan datasets of planning CT covering the nasopharynx and neck from 139 patients with NPC between March 2022 and December 2022 were collected and divided into training, validation, and testing cohorts of 95, 24, and 20 patients, respectively. Ground truth contours of primary gross tumor volume (GTVp), GTVn (divided into GTVn_L in left neck and GTVn_R in right neck), CTVs (including high risk CTV1 contains GTVp and low risk CTV2 contains GTVp and cervical nodal levels) and OARs were delineated and were defined by consensus of two experts. We first proposed a three-dimensional (3D) U-net using GTVp and OARs as experience constrains to guide the automated delineation of GTVn and CTVs. The average Dice similarity coefficient (DSC) and average surface distance (ASD) were used to quantify the performance of the AI tool. Next, five prospective patients were enrolled for clinical evaluation of our AI tool. DSC between automated contours and radiation oncologist-revised contours and time consuming of the revision were record.

RESULTS

Clinical characteristics of 139 retrospective and 5 prospective patients are list in Table 1. In the independent testing set of 20 patients, our AI tool showed high performance in GTVn and CTVs contouring when compared with the ground truth contours. The mean DSC were 0.73 ± 0.07, 0.74 ± 0.05, 0.93 ± 0.03, and 0.88 ± 0.03, and the mean ASD were 1.01 ± 0.43 mm, 1.14 ± 0.61 mm, 0.51 ± 0.13 mm, 1.17 ± 0.43 mm for GTVn_L, GTVn_R, CTV1 and CTV2, respectively. In the five prospective patients, mean DSC were 0.74 ± 0.07, 0.74 ± 0.10, 0.95 ± 0.01 and 0.89 ± 0.04, respectively. The median time consuming for GTVn and CTVs revision was 2minutes and 10 seconds (range, 1 minutes to 3 minutes).

CONCLUSION

The proposed AI tool integrating clinical experience as constrains showed high accuracy for contouring GTVn and CTVs of NPC. With the assistance of AI contours, contouring efficiency could be probably increased, which is promising in online adaptive radiotherapy of NPC.

First Implementation of Full-Workflow Automation for Online Adaptive Radiotherapy of Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The aim of this work is to established the technical characteristics and implementation procedures of an artificial intelligence (AI)-powered radiotherapy workflow that enables full-process automation for online adaptive radiotherapy (ART); and evaluate its feasibility and performance implemented for ART of nasopharyngeal carcinoma (NPC).

MATERIALS/METHODS

This single center, prospective study has been approved by the ethical committee of the institution. The online ART workflow was developed based on a CT-integrated linear accelerator. During the course of radiotherapy, the patient underwent daily pre-treatment fan-beam CT (FBCT) scan. Then the FBCT was automatically registered to the original planning CT and used to assess the need for the patient to implement ART according to radiation oncologist's discretionary. The online ART workflow incorporates critical radiotherapy procedures from re-simulation, auto-segmentation by integrating image fusion and deep learning method, auto-replanning, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch during the whole process.

RESULTS

From 2th April 2022 to 5th January 2023, 20 patients with newly-diagnosed, non-metastatic NPC were enrolled in this study. Only one-time online ART was performed for each patient, because that the appropriate timing for triggering online ART was explored in parallel with this study. According to radiation oncologists' discretionary, the median fraction for performing online ART was at 21 fractions (interquartile range, 19-24 fractions). All patients were well tolerated and successfully completed the treatment. For tumor targets contouring, minor revisions were required for automated contours of the primary gross tumor volume (GTVp) and clinical target volumes (CTVs, including CTV1 and CTV2), with the mean DSC between before and after revision of 0.91±0.042, 0.94 ± 0.042 and 0.91 ± 0.061, respectively; and much more revisions for the automated contours of cervical lymph nodes GTV (GTVn), with the mean DSC of 0.74 ± 0.28. The automated contours of normal tissues were clinically acceptable with little modifications. Median time consuming for auto-segmentation and revision was 9.5 minutes (min). For treatment planning, 18 automated plans (90%) were passed at their first auto-optimization and two plans (10%) were passed after further optimization of the dose coverage of CTVs by physicist; and the median time consuming for auto-planning was 6.2 min. Time consuming for other procedures were as follows: re-simulation, 2.3 min; plan evaluation, 3.3 min; beam delivery, 4.6 min; and the duration of the entire process was 25.9 min, range from 19.4 min to 32.5 min.

CONCLUSION

We successfully established an AI-powered online ART workflow for adaptive radiotherapy of NPC, and confirmed that current auto-segmentation and auto-replanning methods are powered enough to support the clinical application of its online ART.

Preserving Structural Consistency in the Generation of Synthetic CT in Pelvic MR-Only Radiation Treatment Planning

PURPOSE/OBJECTIVE(S)

MR-based synthetic CT (sCT) generation is necessary for MR-only radiotherapy to assist in radiation dose calculation, owing to no electronic density information in MR images. This study investigated the feasibility of synthesizing CT images from magnetic resonance (MR) images using generation antagonism networks (GANs) for MR radiotherapy of rectal cancer. Meanwhile, the transformer module and the contrast learning loss were introduced to improve the sCT.

MATERIALS/METHODS

The data set used in this study was the T2-weighted MR and CT image data of 108 patients with rectal cancer. Three-fold cross-validation was performed on all data sets. The transformer module was introduced into the plain CycleGAN, and the improved Patch Noise Contrastive Estimation (PatchNCE) loss was used as the loss function. The improved PatchNCE loss maintained the structural consistency of the MR and the synthetic CT by ensuring the consistency of the distribution of image patches on the MR-sCT image pair. The 2.5D images were taken as the input of our model, which refers to taking two consecutive adjacent layers in a specific layer. The CT-to-sCT image similarity was evaluated by metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and Structure Similarity Index Measure (SSIM). The sCT dosimetric accuracy was verified against CT-based dose distributions for the photon plan. Relative dose differences in the planning target volume and organs at risk were computed.

RESULTS

The evaluation indicators of sCT images generated by our model were superior to the plain CycleGAN in the results of the three-fold cross-validation. MAE, PSNR and SSIM of our model were 42.850HU, 26.486 and 0.988, respectively, which were superior to 47.129HU, 25.167 and 0.978 of the plain CycleGAN. In addition, sCT generated by our model exhibited good continuity in the axial direction compared with plain CycleGAN. Furthermore, most of the relative differences in the DVH indicators were less than 1%.

CONCLUSION

The accuracy of sCT can be effectively improved by introducing a transformer module and comparative learning loss function. Moreover, all dosimetric differences were within clinically acceptable criteria for photon radiotherapy, demonstrating the feasibility of the MRI-only workflow for patients with rectal cancer.

Cost-Effectiveness of Short-Course Radiotherapy Based Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer in China

PURPOSE/OBJECTIVE(S)

The phase III STELLAR (NCT02533271) trial demonstrated that four cycles of chemotherapy after short-course radiotherapy (SCRT-TNT) were not inferior to the standard care of long-course concurrent radiotherapy (LCRT) in patients with locally advanced rectal cancer (LARC). This study assessed the cost-effectiveness of SCRT-TNT versus LCRT in locally advanced rectal cancer in China on the basis of the STELLAR trial.

MATERIALS/METHODS

A Markov model was used to synthesize the healthcare costs and benefits of LARC patients based on results from the STELLAR trial. The model assumes that LARC who meet the inclusion criteria of the STELLAR trial experience four possible states: No Evidence of Disease (NED), locally recurrence, distant metastases, or any death from rectal cancer or other unrelated causes, where local recurrence continues to be classified as resectable and unresectable. The transition status period is 3 month, and 5 years is used to calculate direct medical costs and health benefits. The probabilities of states transition after SCRT-TNT or LCRT were derived from the results of the STELLAR trial and previous published article (Table.1). Costs were evaluated from the Chinese payer's perspective reported in early 2022 US dollars (US$1 = 6.78 Chinese Yuan). Sensitivity analyses were performed for key variables. Cost-effectiveness was evaluated using the incremental cost-effectiveness ratio and net monetary benefits. Effectiveness was defined as quality-adjusted life-years (QALYs). Willingness-to-pay (WTP) threshold was set at $43500/QALY. Data were collected from October 3, 2020, to September 20, 2021, and analyzed from November 15, 2020, to October 25, 2021.

RESULTS

During the 5-year horizon, for the base case scenario, SCRT-TNT incurred a lower total cost and higher QALYs compared with LCCRT. The total cost was $65767 and QALYs were 1.77 for SCRT-TNT; for LCCRT, the total cost was $72802 and QALYs were 1.64. This resulted in an ICER of -$ 55470.69 per QALY. Therefore, SCRT-TNT was a cost-saving and dominating treatment strategy compared with LCRT. Sensitivity analysis showed that ICERs were most sensitive to the parameters of distant metastases risk after treatment.

CONCLUSION

SCRT-TNT in locally advanced rectal cancer can be a cost-effective alternative to LCRT in China, and should be considered in appropriately selected patients.

A Multicenter Study on Deep Learning for Glioblastoma Auto-Segmentation with Prior Knowledge in Multimodal Imaging

PURPOSE/OBJECTIVE(S)

A precise radiotherapy plan is required to ensure accurate delineation of gross tumor volumes (GTV) and clinical target volumes (CTV1 and CTV2) of glioblastomas (GBMs). However, traditional manual delineation is labor intensive and highly dependent on oncologists' experience. To construct and evaluate a deep learning-based automatic delineation method using prior knowledge in multimodal medical imaging to automate precise GTV, CTV1 and CTV2 contouring in GBM patients.

MATERIALS/METHODS

We retrospectively collected the CT and MRI scans of 55 eligible patients with histologically proven high-grade glioma (HGG) from an institute, these scans were performed with non-enhanced CT (CT), contrast-enhanced T1-weighted (T1C) and T2-FLAIR (T2F) sequences. We proposed a two-stage automatic segmentation framework (PKMI-Net) for GTV, CTV1 and CTV2 based on deep learning using prior knowledge in multimodal medical imaging, and its segmentation performance was evaluated with dice similarity coefficient (DSC), 95% Harsdorff distance (HD95), average surface distance (ASD) and relative volume difference (RVD). To further investigate the generalizability of our method, we designed and conducted two evaluation strategies (Mix and Cross) on four multicenter datasets (including 55 patients, 37 patients, 21 patients and 35 patients).

RESULTS

The evaluation results with an 11-patient test set from the single institute were summarized in Table 1, the proposed method demonstrated the best accuracy in segmenting, respectively, GTV, CTV1 and CTV, achieving a DSC of 0.94, 0.95 and 0.92; HD95 of 2.07 mm, 1.18 mm and 3.80 mm; ASD of 0.69 mm, 0.39 mm and 1.13 mm and RVE of 5.50%, 3.97% and 9.68%. In the multicenter evaluation, the segmentation performance of our method implemented with the Cross strategy was comparable to that with the Mix strategy, demonstrating that our method had high and stable generalizability across multicenter datasets in automatically segmenting GTV, CTV1 and CTV2.

CONCLUSION

Our proposed method achieved promising results in automatically segmenting gliomas across various datasets, which could improve the quality and efficiency of glioblastoma radiotherapy.

Virtual-Collimator Based Spatial Dose Modulation for Proton GRID Therapy

PURPOSE/OBJECTIVE(S)

Compared to conventional proton therapy, the proton GRID therapy can substantially improve normal tissue protection (with the delivery of spatially-modulated peak-valley dose pattern to normal tissues) while maintaining the tumor control efficacy (with the delivery of uniform dose pattern to tumor targets). The realization of proton GRID often relies on the use of physical collimators to shape the spatial dose distribution. However, the physical collimator may increase neutron dose, decrease delivery efficiency, and limit the freedom for patient positioning. Here we propose a virtual-collimator (VC) method for proton GRID. This new approach can generate peak-to-valley pattern with high peak-to-valley dose ratio (PVDR), without using a physical collimator.

MATERIALS/METHODS

The principle behind the VC method to modulate the spatial dose distribution consists of two major steps: (1) the primary beam is essentially halved, i.e., the beamlets are interleaved, so that the organ-at-risk (OAR) plane has the peak-valley dose pattern, while the target plane also has the valley dose; (2) the complementary beam is added with half complementary beamlets to fill in the previously valley-dose positions at the target plane, so that the target dose is uniform, while on the other hand, the complementary beam is angled slightly from the primary beam, so that the OAR still has the peak-valley dose pattern. Moreover, on top of VC, we also utilize sparsity regularization method using total variation and L1 sparsity (TVL1) to further jointly optimize PVDR and dose objectives, namely VC-TVL1.

RESULTS

VC and VC-TVL1 were validated in comparison with conventional proton GRID treatment planning method via IMPT ("CONV") and TVL1-based proton GRID treatment planning method without VC ("TVL1"), for a prostate case with single-beam (270° only) or two-beam (90° and 270°) scenarios. As shown in the table, the results show that VC can indeed modulate spatial dose with higher PVDR than CONV or even TVL1. VC had higher spatial modulation frequency with smaller peak-to-peak distance than TVL1. Moreover, VC+TVL1, as the synergy of VC and TVL1, further improved PVDR from VC or TVL1 alone.

CONCLUSION

A new way to deliver proton GRID therapy without a physical collimator is developed using the VC method. The VC method can be synergized with TVL1 optimization algorithm to further jointly optimize PVDR and dose objectives.

Prediction of Response to Neoadjuvant Chemoradiotherapy Combined with Pembrolizumab in Esophageal Squamous Cell Carcinoma with CT/FDG PET Radiomic Signatures Based on Machine Learning Classification

PURPOSE/OBJECTIVE(S)

PALACE-1 trial has confirm that the addition of pembrolizumab to neoadjuvant chemoradiotherapy (NCRT) improves the pathological complete response(pCR) for esophageal squamous cell carcinoma (ESCC), which might be a novel treatment strategy for ESCC. In the present study, we aim to establish a machine learning model to predict the local response to NCRT+ pembrolizumab for ESCC by using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) and contrast-enhanced plan CT images.

MATERIALS/METHODS

A total of 65 cases treated with NCRT+ pembrolizumab followed by surgery were prospectively enrolled for analysis from 2019-2022. Each patient contains a contrast-enhanced plan CT and FDG PET images. 52 patients were randomly divided into training set and 13 patients were used as test set. The Extraction of radiomics features was performed using an open-source Python library PyRadiomics automatically. Features were computed according to the radiologist-drawn ROIs on both CT and PET images. In the feature selection stage least absolute shrinkage and selection operator (LASSO) was utilized on CT features and PET features separately. Four different machine learning models were implemented: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and XGBoost (XGB). The features selected by LASSO regression were used as model input and the output of the model is "pCR" or "non-pCR". To find the optimal parameter, the 5-fold cross-validation method was used in the training stage. In this study, we use accuracy, sensitivity and specificity as the metrics to evaluate the performance of the model on the testing cohort. The predictive performance of the model was assessed using the area under curve (AUC) of the receiver operating characteristics curve (ROC).

RESULTS

Of the 65 cases treated with NCRT+pembrolizumab, 35 patients archived pCR (53.8%), and 30 archived non-pCR. 1684 radiomics features were extracted from each case, and half of them (842 features) were from CT and others were from PET. Among the machine learning models mentioned above SVM achieves the most promising performance on the evaluation metrics. Accuracy, sensitivity, specificity and AUC score on test set were 0.692, 0.833, 0.571 and 0.786 for CT features and 0.615, 0.667, 0.571 and 0.762 for PET features, respectively. For CT+FDG PET fused features accuracy, sensitivity, specificity and AUC score on test set were 0.769, 0.667, 0.857 and 0.833.

CONCLUSION

In this study, we performed several different machine learning models to predict the response to NCRT+ pembrolizumab among ESCC based on the extracted radiomics features from CT and FDG PET images. The best-performing model based on radiomics features of CT and PET images could identify non-pCR to NCRT + pembrolizumab in EC patients.